Air Sampling of Toluene
I am an industrial hygienist conducting a personal sampling on an employee overseeing a production process involving the use of toluene. From the information I have with me, I understand that the safety and health administration has allowed an exposure limit of 200 parts per million (ppm). At this point, I have measured the time-weighted average over a period of eight hours and found it to be 196.875 ppm. Looking at the information I have, one would out rightly conclude that toluene exposure in the plant is below the recommended one because 196.875 is less than 200 (Haight, 2012). However, this assumption might be incorrect. For this reason, I need to go a step further to find out whether my findings are truthful or untruthful before I decide what to tell the plant management team. In relation to this fact, I would tell the plant management team that I cannot conclude at this point because I need to go a step further to establish whether my findings are truthful or untruthful.
In order to do this, I would start by presuming that the time weighted average for the sample would be less or equal to 200 ppm. This means that I would presume that the sample average would be lower than the recommended limit. After presuming this, then I would proceed to test whether this hypothesis is true or not. In case this hypothesis would be true, then I would conclude that my assumption would be right. On the contrary, in case my hypothesis would be untrue, then I would conclude that my assumptions were wrong (Bird, 2014).
The testing process would involve collecting the data, recording the data and finding the variability of the data because I do not know its variability. Once I calculate the variance and subsequently the standard deviation of the sample, then I would be able to determine whether my hypothesis would be truthful or untruthful. By this I mean that I would be able to calculate the test statistic and test the hypothesis from the data that I would collect and analyze (Bird, 2014). In order to be certain that my data would be reflecting the situation as it would be in the plant, I would choose a significance level of either 5% or 2.5% and use it in my analysis. The significance level would help me to determine when to reject or accept the null hypothesis.
My argument is that based on the available information, it might be difficult to tell the situation as it is in the plant even if the data shows that one should conclude that the toluene exposure is below the recommended one. For this reason, I might not be quick to inform the plant management team that the toluene exposure is below the recommended limit. On the contrary, I would estimate some parameters of toluene exposure in the plant from the sample I have and then make my conclusion based on the findings (Bird, 2014).
In spite of the above fact, I would tell the plant management that the toluene exposure in the plant should be maintained within the recommended limit. This means that if the plant would be releasing more toluene than it should release, then the management team should ensure that employees reduce it below the recommended limit. Otherwise, the plant would be at the risk of being charged in the courts of law and risk legal charges (Haight, 2012). For this reason, I would ensure that the plant management team understands the risk of releasing more toluene than the recommended one and work towards observing the safety standards.
Bird, J. (2014).Higher Engineering Mathematics. New York: Routledge.
Haight, J. (2012). Recognition, evaluation, and control of workplace health hazards. Des Plaines, Ill: American Society of Safety Engineers.