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

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.