Dataset Information

Carbon Nanotubes: Probabilistic Approach for Occupational Risk Assessment.

ABSTRACT: In this study, the occupational risk assessment of carbon nanotubes (CNTs) was performed by means of a probabilistic approach. Chronic and subchronic inhalation exposure studies were retrieved during the hazard identification phase of the study. These studies were then used to obtain a guidance value (BMC_h, expressed as a lognormal distribution with geometric mean ± geometric standard deviation = 10.0 ± 4.2 µg/m³) for occupational inhalation exposure to CNTs. An exposure scenario was selected from the scientific literature: three different work events (WEs) related to the production of conductive films were considered: (WE1) manufacturing of single walled carbon nanotubes films during normal operation using local exhaust ventilation (LEV); (WE2) manufacturing of SWCNT film without LEV; and (WE3) cleaning of one of the reactors. For each WE, a probability distribution function was applied, considering exposure expressed as mass concentration, as derived from three different measurement techniques. The ratio of the exposure and the BMC_h distributions (i.e., the risk characterization ratio-RCR) was used to calculate the probability of occurrence of a relevant occupational risk. All the considered WEs indicated the presence of a risk (i.e., RCR distributions ? 1); however, only WE2 resulted in a statistically significant level of risk.

SUBMITTER: Spinazze A

PROVIDER: S-EPMC7916016 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Carbon Nanotubes: Probabilistic Approach for Occupational Risk Assessment.

Spinazzè Andrea A Zellino Carolina C Borghi Francesca F Campagnolo Davide D Rovelli Sabrina S Keller Marta M Fanti Giacomo G Cattaneo Andrea A Cavallo Domenico M DM

Nanomaterials (Basel, Switzerland) 20210205 2

In this study, the occupational risk assessment of carbon nanotubes (CNTs) was performed by means of a probabilistic approach. Chronic and subchronic inhalation exposure studies were retrieved during the hazard identification phase of the study. These studies were then used to obtain a guidance value (BMC<sub>h</sub>, expressed as a lognormal distribution with geometric mean ± geometric standard deviation = 10.0 ± 4.2 µg/m<sup>3</sup>) for occupational inhalation exposure to CNTs. An exposure sc ...[more]

PMID: 33562871

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Project description:BACKGROUND:Recent cross-sectional epidemiologic studies have examined the association between human health effects and carbon nanotube and nanofiber (CNT/F) workplace exposures. However, due to the latency of many health effects of interest, cohort studies with sufficient follow-up will likely be needed. The objective of this study was to identify workplace determinants that contribute to exposure and develop predictive models to estimate CNT/F exposures for future use in epidemiologic studies. METHODS:Exposure measurements were compiled from 15 unique facilities for the metrics of elemental carbon (EC) mass at both the respirable and inhalable aerosol size fractions as well as a quantitative analysis performed by transmission electron microscopy (TEM). These metrics served as the dependent variables in model development. Repeated personal samples were collected from most of the 127 CNT/F worker participants for 252 total observations. Determinants were categorized as company-level or worker-level and used to describe the exposure relationship within the dependent variables. The influence of determinants on variance components was explored using mixed linear models that utilized a backwards stepwise selection process with a lowering of the AIC for model determinant selection. Additional ridge regression models were created that examined predictive performance with and without all two-way interactions. Cross-validation was performed on each model to evaluate the generalizability of its predictive capabilities while predictive performance was evaluated according to the corresponding R2 value and root mean square error (RMSE). RESULTS:Determinants at the company-level that increased exposure included an inadequate or semi-adequate engineering control rating, increasing average CNT/F diameter/length, daily quantities of material handled from 101 g to >1 kg and >1 kg, the use of CNF materials, the industry type of hybrid producer/user, and the expert assessment of a high exposure potential. Worker-level determinants associated with higher exposure included handling the dry-powdered form of CNT/F, handling daily quantities of material >1 kg, direct/indirect exposure, having the job title of engineer, using a respirator, using a ventilated or unventilated enclosure, and the job task of powder handling. The mixed linear models explained >60% of the total variance when using all company- and worker-level determinants to create the three exposure models. The cross-validated RMSE values for each of the three mixed models ranged from 2.50 to 4.23. Meanwhile, the ridge regression models, without all two-way interactions, estimated cross-validated RMSE values of 2.85, 2.23, and 2.76 for the predictive models of inhalable EC, respirable EC, and TEM, respectively. CONCLUSIONS:The ridge regression models demonstrated the best performance for predicting exposures to CNT/F for each exposure metric, although they only provided a modest predictive capability. Therefore, it was concluded that the models alone would not be adequate in predicting workplace exposures and would need to be integrated with other methods.

Project description:BackgroundA diverse class of engineered nanomaterials (ENMs) exhibiting a wide array of physical-chemical properties that are associated with toxicological effects in experimental animals is in commercial use. However, an integrated framework for human health risk assessment (HHRA) of ENMs has yet to be established. Rodent 2-year cancer bioassays, clinical chemistry, and histopathological endpoints are still considered the 'gold standard' for detecting substance-induced toxicity in animal models. However, the use of data derived from alternative toxicological tools, such as genome-wide expression profiling and in vitro high-throughput assays, are gaining acceptance by the regulatory community for hazard identification and for understanding the underlying mode-of-action. Here, we conducted a case study to evaluate the application of global gene expression data in deriving pathway-based points of departure (PODs) for multi-walled carbon nanotube (MWCNT)-induced lung fibrosis, a non-cancer endpoint of regulatory importance.MethodsGene expression profiles from the lungs of mice exposed to three individual MWCNTs with different physical-chemical properties were used within the framework of an adverse outcome pathway (AOP) for lung fibrosis to identify key biological events linking MWCNT exposure to lung fibrosis. Significantly perturbed pathways were categorized along the key events described in the AOP. Benchmark doses (BMDs) were calculated for each perturbed pathway and were used to derive transcriptional BMDs for each MWCNT.ResultsSimilar biological pathways were perturbed by the different MWCNT types across the doses and post-exposure time points studied. The pathway BMD values showed a time-dependent trend, with lower BMDs for pathways perturbed at the earlier post-exposure time points (24 h, 3d). The transcriptional BMDs were compared to the apical BMDs derived by the National Institute for Occupational Safety and Health (NIOSH) using alveolar septal thickness and fibrotic lesions endpoints. We found that regardless of the type of MWCNT, the BMD values for pathways associated with fibrosis were 14.0-30.4 μg/mouse, which are comparable to the BMDs derived by NIOSH for MWCNT-induced lung fibrotic lesions (21.0-27.1 μg/mouse).ConclusionsThe results demonstrate that transcriptomic data can be used to as an effective mechanism-based method to derive acceptable levels of exposure to nanomaterials in product development when epidemiological data are unavailable.

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Carbon Nanotubes: Probabilistic Approach for Occupational Risk Assessment.

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Carbon Nanotubes: Probabilistic Approach for Occupational Risk Assessment.

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