Modeling of High Nanoparticle Exposure in an Indoor Industrial Scenario with a One-Box Model

Bibliographic Data

ID15467213
AuthorsCarla Ribalta (0000-0002-0842-5842, Institute of Environmental Assessment and Water Research, corresponding author), Antti Joonas Koivisto (0000-0002-6769-1999, University of Helsinki), Apostolos Salmatonidis (0000-0002-9999-3836, Institute of Environmental Assessment and Water Research), Ana López-Lilao (0000-0002-6984-9155, Universitat Jaume I), E Monfort (0000-0003-3995-2378, Universitat Jaume I), Mar Viana (0000-0002-4073-3802, Institute of Environmental Assessment and Water Research)
Year2019
Volume16
Issue10
Pages1695-1695
Publication date2019-05-14
Open AccessYes
TypeARTICLE
VenueInternational Journal of Environmental Research and Public Health (JOURNAL)
Journal identifiersISSN: 1661-7827 • E-ISSN: 1660-4601
PublisherMultidisciplinary Digital Publishing Institute (PUBLISHER • CH)
DOI10.3390/ijerph16101695
PMID31091807
OpenAlexW2945060712
LanguageEN
Citations received2
References cited56

Mass balance models have proved to be effective tools for exposure prediction in occupational settings. However, they are still not extensively tested in real-world scenarios, or for particle number concentrations. An industrial scenario characterized by high emissions of unintentionally-generated nanoparticles (NP) was selected to assess the performance of a one-box model. Worker exposure to NPs due to thermal spraying was monitored, and two methods were used to calculate emission rates: the convolution theorem, and the cyclic steady state equation. Monitored concentrations ranged between 4.2 × 10 4 -2.5 × 10 5 cm -3 . Estimated emission rates were comparable with both methods: 1.4 × 10 11 -1.2 × 10 13 min -1 (convolution) and 1.3 × 10 12 -1.4 × 10 13 min -1 (cyclic steady state). Modeled concentrations were 1.4-6 × 10 4 cm -3 (convolution) and 1.7-7.1 × 10 4 cm -3 (cyclic steady state). Results indicated a clear underestimation of measured particle concentrations, with ratios modeled/measured between 0.2-0.7. While both model parametrizations provided similar results on average, using convolution emission rates improved performance on a case-by-case basis. Thus, using cyclic steady state emission rates would be advisable for preliminary risk assessment, while for more precise results, the convolution theorem would be a better option. Results show that one-box models may be useful tools for preliminary risk assessment in occupational settings when room air is well mixed

Atmospheric sciences · Box model · Environmental health · Occupational exposure · Physics · Air Quality and Health Impacts · Computer Science · Environmental Impact and Sustainability · Environmental Science · Medicine · Vehicle emissions and performance

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