The identification of the noise can be done by taking a daily walk around the office or appointing special staffs who can go on regular inspections and observe things. This will surely help in the assessment of noise and the noise source. Steps can hence be taken to avoid the health hazards that can be caused due to noise pollution (Cheer & Elliott., 2015).
The next important step that is to be taken is to check whether the individuals are really at risk. Once the risk factors have been identified, a noise assessment has to be done. There are many factors that need to be taken into account here, like the type of workers, the duration of the shifts, the equipments that are being used in the work, environmental factors.
Measures to control noise pollution
The most effective and the long term solutions for dealing with this problem are to buy those kinds of equipments that are noise free. In other words, the users must take a proper opinion from suppliers and purchase those plants that will not emit noise during their operations (Aslam et al., 2015). The users of those equipments must conduct meetings with the suppliers, in order to gain detailed information about the likely sound emissions that the usage of those implements might cause. The users must make sure to purchase their equipments only from those suppliers who can provide a better noise control system. Only those machineries must be used that has noise control as one of its most important function.
Apart from this, all the tools and implements that are being used should be passed thorough tests in order to check whether it can cause any harm to the users or not. Other preventive measures can also be utilized like using ear plugs, whenever working with machineries of high volume and high frequency (Ferrer et al., 2015). The users must make sure that if they are using any high volume emitting tools, they must always do it in some sound proof enclosures. There can also be the construction of screens so that the sound does not directly hit the ears of the users.
However, administrative control must be ensured by the managers in the heavy industrial plant that has huge noise exposure (Shah et al., 2014). This can be done by providing sufficient breaks to the workers to rest in some quite areas who are exposed to high frequency sound. Shift timing schedules are to be arranged in such a way so that the workers are not over exposed to those heavy noises. There must also be strict prohibitions maintained for the workers to enter in the noise prone areas except those who are specially trained.
Instances when we are overwhelmed by false positivities and thus ignore the true incidents
Often during any disaster like an explosion in any mining area, there is a huge noise and also a huge pollution. Soon after the damage and the shock are initially under control, people think that the danger is now over and they can again get back to the normal order of doing things. However, they also fail to understand the gravity of the situation and ignore the warning that such disasters might happen again causing even greater damage (Aslam & Raja., 2015).
Hence, in order to strike a balance between this, people must be very alert. They must be able to identify the severity of the disaster. They must also understand that there is the probability of the disaster happening again. So, they must start taking the necessary precautions before hand like using life jackets, wearing ear plugs and using sound proof implements.
Aslam, M. S., & Raja, M. A. Z. (2015). A new adaptive strategy to improve online secondary path modeling in active noise control systems using fractional signal processing approach. Signal Processing, 107, 433-443.
Cheer, J., & Elliott, S. J. (2015). Multichannel control systems for the attenuation of interior road noise in vehicles. Mechanical Systems and Signal Processing, 60, 753-769.g
Ferrer, M., de Diego, M., Piñero, G., & Gonzalez, A. (2015). Active noise control over adaptive distributed networks. Signal Processing, 107, 82-95.
Shah, S. M., Samar, R., Raja, M. A. Z., & Chambers, J. A. (2014). Fractional normalised filtered-error least mean squares algorithm for application in active noise control systems. Electronics Letters, 50(14), 973-975.