Development of polyurethane foam masks as air samplers for assessing personal exposure to volatile and semi-volatile organic compounds

Abstract

Air pollution is a topic of great concern, and the complex indoor environment can be a major source of many airborne pollutants such as volatile and semi-volatile organic compounds (VOCs and SVOCs) which have diverse presence in air and adverse health impacts. To develop a strategy for reducing human exposure to VOCs and SVOCs, the first step is to accurately measure and record the exposure to these compounds. Currently, bio-marker monitoring, active air sampling, and passive air sampling are the 3 common methods for estimating personal exposure to VOCs and SVOCs. However, each of them has drawbacks in different perspectives, which highlights the need for developing alternative air sampling approaches. This study aims to develop polyurethane foam (PUF) face mask as a novel personal air sampler which is simple, inexpensive, unobstructive, and can provide quantitative results with relatively high time resolution. In this study, a pre-cleaning procedure was first developed to reduce residue SVOCs (~80 to 100%) on the untreated PUF face masks. Afterwards, collection efficiencies of target VOCs and SVOCs (~50% to 96%) for the PUF mask sampling method was determined using multi-layer mask sampling method and further validated by parallel study with XAD-2 sorbent tube-based active air sampler. The optimized PUF mask sampling method was then used to collect air samples at 34 different premises in Hong Kong. Results show that the levels of model VOCs and SVOCs varies in different microenvironments, indicating that PUF mask sampling method has spatial sensitivity and may yield highly individualized data. It is anticipated that this novel air sampling method may provide a simple approach for assessing personal exposure to a wide array of VOCs and SVOCs, which will also be useful for future source apportionment studies and implementation of indoor air quality standards.

Date of Award	2022
Original language	English
Awarding Institution	The Hong Kong University of Science and Technology
Supervisor	Wan CHAN (Supervisor)