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

Abstract

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 × 104–2.5 × 105 cm−3. Estimated emission rates were comparable with both methods: 1.4 × 1011–1.2 × 1013 min−1 (convolution) and 1.3 × 1012–1.4 × 1013 min−1 (cyclic steady state). Modeled concentrations were 1.4-6 × 104 cm−3 (convolution) and 1.7–7.1 × 104 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.

Evaluation of One- and Two-Box Models as Particle Exposure Prediction Tools at Industrial Scale

“…In the current study, inclusion of background air mass-concentrations had only low impact on modelled inhalable and respirable concentrations (Figure 3), even though the workplace air was affected by other background activities including diesel-powered forklifts. Thus, including outdoor concentration seems to be in this case less crucial and in contrast to findings in Ribalta et al [13,36].…”

“…Model performance was evaluated according to the benchmark for the ratio modelled/measured concentrations of 0.5-2 reported by Jayjock et al [9] which has been used by several authors [10,13,14,36]. The following nomenclature was used in this study (i) underestimation (ratio < 0.5), (ii) accurate estimation (ratio 0.5-2), (iii) slight overestimation (ratio 2-5) and (iv) high overestimation (ratio > 5).…”

Characterizing the Chemical Profile of Incidental Ultrafine Particles for Toxicity Assessment Using an Aerosol Concentrator

“…The case study selected was a thermal spraying facility where two different types of technologies were used to spray ceramic coatings ( Salmatonidis et al , 2019a ), in the framework of SIINN-ERANET project CERASAFE (Safe Production and Use of Nanomaterials in the Ceramic Industry). Advanced ceramic materials and processing technologies have a strong potential for incidental formation and release of UFP into workplace air ( Fonseca et al , 2015 ; Viana et al , 2017 ; Ribalta et al , 2019b ; Salmatonidis et al, 2019a , 2019b ; Bessa et al , 2020 ). The use of the VACES system provided a unique opportunity to collect particles, simultaneously, on filter substrates for chemical characterization and as suspensions for toxicity assessments (discussed elsewhere; Bessa et al , 2021 ).…”

Exposure Models for REACH and Occupational Safety and Health Regulations

“…However, even when all key information is known to a high degree, there can still be challenges in predicting the exposure levels accurately. For example, Ribalta et al [19] used a single-compartment mass balance model of a thermal spray scenario, where the exposure was underestimated by between 30% and 80%, and the best predictions were achieved only after more advanced re-modelling. Some of these issues may be related to the correct determination of source strengths, which can be difficult to achieve.…”