Data Analysis to Evaluate the Performance of Breathing Masks used for Filtering Nano-Level Particles at Manufacturing Sites

The work performed in this research aims to evaluate the performance of commercially available breathing masks in filtering airborne nanoparticles at manufacturing sites. Nanoparticles are found virtually anywhere, from dust in a worksite to a simple sneeze. Therefore, they pose a substantial threat to human health as their velocity and volatility are high. This research analyzes if current efforts of breathing masks to hinder nanoparticles are effective, especially at manufacturing sites. Data has been collected in order to analyze the behavior of nanoparticles and to measure nanoparticle levels at manufacturing sites and its working environment. Data is statistical in nature and needs to be streamlined in order to be presentable. In the present thesis work, the data is analyzed extensively using statistical methods so that meaningful conclusions can be drawn from it. More specifically, data analytics is applied in order to conclude if the data collected at the manufacturing sites to evaluate breathing masks shows consistencies regarding filtration. Therefore, if the data is consistent throughout the analysis, then the behavior of nanoparticles can be better understood to draw conclusions on the effectiveness of filtration methods in construction sites. Furthermore, we seek a trend for future applications of such masks. That is, by understanding the effectiveness of masks and their behavior, conclusions can be drawn as to how nanoparticles, such as viruses, can diffuse and the optimum way to reduce their penetration. This is a unique work as none of the researchers have done the live measurements of nanoparticles simultaneously to evaluate the effectiveness of breathing masks. Also, based on this research, conclusions are drawn on whether screening molecules and equipment are effective to filter particles at nanoscales at the desired level