Using a modified electrical aerosol detector to predict nanoparticle exposures to different regions of the respiratory tract for workers in a carbon black manufacturing industry

Ying Fang Wang, Peng-Chi Tsai, Chun Wan Chen, Da Ren Chen, Der Jen Hsu

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)

Abstract

The present study was set out to characterize nanoparticle exposures in three selected workplaces of the packaging, warehouse, and pelletizing in a carbon black manufacturing plant using a newly developed modified electrical aerosol detector (MEAD). For confirmation purposes, the MEAD results were compared with those simultaneously obtained from a nanoparticle surface area monitor (NSAM) and a scanning mobility particle sizer (SMPS). We found that workplace background nanoparticle concentrations were mainly coming from the outdoor environment. Size distributions of nanoparticles for the three selected process areas during the work hours were consistently in the form of bimodel. Unlike nanoparticles of the second mode (simply contributed by the process emissions), particles of the first mode could be also contributed by the forklift exhaust or fugitive emissions of heaters. The percents of nanoparticles deposited on the alveolar (A) region were much higher than the other two regions of the head airway (H), tracheobronchial (TB) for all selected workplaces in both number and surface area concentrations. However, significant differences were found in percents of nanoparticles deposited on each of the three regions while different exposure metrics were adopted. Both NSAM and MEAD obtained quite comparable results. No significant difference can be found between the results obtained from SMPS and MEAD after being normalized. Considering the MEAD is less expensive, less bulky, and easy to use, our results further support the suitability of using MEAD in the field for nanoparticle exposure assessments.

Original languageEnglish
Pages (from-to)6767-6774
Number of pages8
JournalEnvironmental Science and Technology
Volume44
Issue number17
DOIs
Publication statusPublished - 2010 Sep 1

All Science Journal Classification (ASJC) codes

  • Chemistry(all)
  • Environmental Chemistry

Fingerprint Dive into the research topics of 'Using a modified electrical aerosol detector to predict nanoparticle exposures to different regions of the respiratory tract for workers in a carbon black manufacturing industry'. Together they form a unique fingerprint.

Cite this