Assessing Exposure and Evaluating the Efficiency of Filters and Masks in Capturing Airborne Nanoparticles

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Description
The prevalence and unique properties of airborne nanoparticles have raised concerns regarding their potential adverse health effects. Despite their significance, the understanding of nanoparticle generation, transport, and exposure remains incomplete. This study first aimed to assess nanoparticle exposure in indoor

The prevalence and unique properties of airborne nanoparticles have raised concerns regarding their potential adverse health effects. Despite their significance, the understanding of nanoparticle generation, transport, and exposure remains incomplete. This study first aimed to assess nanoparticle exposure in indoor workplace environments, in the semiconductor manufacturing industry. On-site observations during tool preventive maintenance revealed a significant release of particles smaller than 30 nm, which subsequent instrumental analysis confirmed as predominantly composed of transition metals. Although the measured mass concentration levels did not exceed current federal limits, it prompted concerns regarding how well filter-based air sampling methods would capture the particles for exposure assessment and how well common personal protective equipment would protect from exposure. To address these concerns, this study evaluated the capture efficiency of filters and masks. When challenged by aerosolized engineered nanomaterials, common filters used in industrial hygiene sampling exhibited capture efficiencies of over 60%. Filtering Facepiece Respirators, such as the N95 mask, exhibited a capture efficiency of over 98%. In contrast, simple surgical masks showed a capture efficiency of approximately 70%. The experiments showed that face velocity and ambient humidity influence capture performance and mostly identified the critical role of mask and particle surface charge in capturing nanoparticles. Masks with higher surface potential exhibited higher capture efficiency towards nanoparticles. Eliminating their surface charge resulted in a significantly diminished capture efficiency, up to 43%. Finally, this study characterized outdoor nanoparticle concentrations in the Phoenix metropolitan area, revealing typical concentrations on the order of 10^4 #/cm3 consistent with other urban environments. During the North American monsoon season, in dust storms, with elevated number concentrations of large particles, particularly in the size range of 1-10 μm, the number concentration of nanoparticles in the size range of 30-100 nm was substantially lower by approximately 55%. These findings provide valuable insights for future assessments of nanoparticle exposure risks and filter capture mechanisms associated with airborne nanoparticles.
Date Created
2023
Agent

Stochastic Modeling of Loss Reserves Using Bayesian Monte Carlo Markov Chain

Description

The Mack model and the Bootstrap Over-Dispersed Poisson model have long been the primary modeling tools used by actuaries and insurers to forecast losses. With the emergence of faster computational technology, new and novel methods to calculate and simulate data

The Mack model and the Bootstrap Over-Dispersed Poisson model have long been the primary modeling tools used by actuaries and insurers to forecast losses. With the emergence of faster computational technology, new and novel methods to calculate and simulate data are more applicable than ever before. This paper explores the use of various Bayesian Monte Carlo Markov Chain models recommended by Glenn Meyers and compares the results to the simulated data from the Mack model and the Bootstrap Over-Dispersed Poisson model. Although the Mack model and the Bootstrap Over-Dispersed Poisson model are accurate to a certain degree, newer models could be developed that may yield better results. However, a general concern is that no singular model is able to reflect underlying information that only an individual who has intimate knowledge of the data would know. Thus, the purpose of this paper is not to distinguish one model that works for all applicable data, but to propose various models that have pros and cons and suggest ways that they can be improved upon.

Date Created
2023-05
Agent