BACKGROUND: Relationships among portable scanning mobility particle sizer (P-SMPS), condensation particle counter (CPC), and surface area monitor (SAM), which are different metric measurement devices, were investigated, and two widely used research grade (RG)-SMPSs were compared to harmonize the measurement protocols. METHODS: Pearson correlation analysis was performed to compare the relation between P-SMPS, CPC, and SAM and two common RG-SMPS. RESULTS: For laboratory and engineered nanoparticle (ENP) workplaces, correlation among devices showed good relationships. Correlation among devices was fair in unintended nanoparticle (UNP)-emitting workplaces. This is partly explained by the fact that shape of particles was not spherical, although calibration of sampling instruments was performed using spherical particles and the concentration was very high at the UNP workplaces to allow them to aggregate more easily. Chain-like particles were found by scanning electron microscope in UNP workplaces. The CPC or SAM could be used as an alternative instrument instead of SMPS at the ENP-handling workplaces. At the UNP workplaces, where concentration is high, real-time instruments should be used with caution. There are significant differences between the two SMPSs tested. TSI SMPS showed about 20% higher concentration than the Grimm SMPS in all workplaces. CONCLUSIONS: For nanoparticle measurement, CPC and SAM might be useful to find source of emission at laboratory and ENP workplaces instead of P-SMPS in the first stage. An SMPS is required to measure with high accuracy. Caution is necessary when comparing data from different nanoparticle measurement devices and RG-SMPSs.
Calibration ; Nanoparticles*

Background: Though the artificial neural network (ANN) technique has been used to predict noise-induced hearing loss (NIHL), the established prediction models have primarily relied on cross-sectional datasets, and hence, they may not comprehensively capture the chronic nature of NIHL as a disease linked to long-term noise exposure among workers. Methods: A comprehensive dataset was utilized, encompassing eight-year longitudinal personal hearing threshold levels (HTLs) as well as information on seven personal variables and two environmental variables to establish NIHL predicting models through the ANN technique. Three subdatasets were extracted from the afirementioned comprehensive dataset to assess the advantages of the present study in NIHL predictions. Results: The dataset was gathered from 170 workers employed in a steel-making industry, with a median cumulative noise exposure and HTL of 88.40 dBA-year and 19.58 dB, respectively. Utilizing the longitudinal dataset demonstrated superior prediction capabilities compared to cross-sectional datasets. Incorporating the more comprehensive dataset led to improved NIHL predictions, particularly when considering variables such as noise pattern and use of personal protective equipment. Despite fluctuations observed in the measured HTLs, the ANN predicting models consistently revealed a discernible trend. Conclusions A consistent correlation was observed between the measured HTLs and the results obtained from the predicting models. However, it is essential to exercise caution when utilizing the model-predicted NIHLs for individual workers due to inherent personal fluctuations in HTLs. Nonetheless, these ANN models can serve as a valuable reference for the industry in effectively managing its hearing conservation program.