{"title":"The Nano Exposure Quantifier: a quantitative model for assessing nanoparticle exposure in the workplace.","authors":"Ruby Vermoolen, Remy Franken, Tanja Krone, Neeraj Shandilya, Henk Goede, Hasnae Ben Jeddi, Eelco Kuijpers, Calvin Ge, Wouter Fransman","doi":"10.1093/annweh/wxae104","DOIUrl":null,"url":null,"abstract":"<p><p>Exposure to manufactured nanomaterials (MNs) is a growing concern for occupational health and safety. Reliable methods for assessing and predicting MN exposure are essential to mitigate associated risks. This study presents the development of the Nano Exposure Quantifier (NEQ), a mechanistic model designed to assess airborne MN exposure in the workplace. By utilizing a dataset of 128 MN measurements from existing exposure studies, the model demonstrates its effectiveness in estimating MN exposure levels for particles smaller than 10 µm. The NEQ provides estimates in terms of particle number concentration accompanied by a 95% confidence interval (CI), enabling a comprehensive assessment of MN exposure. The NEQ includes 2 quantitative models: a simplified tier 1 model and a more comprehensive tier 2 model. Both tier 1 and tier 2 models exhibit robust performance, with correlation coefficients (r) of 0.57 and 0.62, respectively. The models exhibit a moderate level of error, as indicated by residuals' standard deviation of 4.10 for tier 1 and 3.90 for tier 2. The tier 1 model demonstrates a slightly higher overestimation bias (1.15) compared to the tier 2 model (0.54). Overall, the NEQ offers a practical and reliable approach for estimating MN exposure in occupational settings. Future validation studies will investigate the impact of initial calibration efforts, heteroscedasticity, and further refine the model's accuracy.</p>","PeriodicalId":8362,"journal":{"name":"Annals Of Work Exposures and Health","volume":" ","pages":""},"PeriodicalIF":1.8000,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals Of Work Exposures and Health","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1093/annweh/wxae104","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
引用次数: 0
Abstract
Exposure to manufactured nanomaterials (MNs) is a growing concern for occupational health and safety. Reliable methods for assessing and predicting MN exposure are essential to mitigate associated risks. This study presents the development of the Nano Exposure Quantifier (NEQ), a mechanistic model designed to assess airborne MN exposure in the workplace. By utilizing a dataset of 128 MN measurements from existing exposure studies, the model demonstrates its effectiveness in estimating MN exposure levels for particles smaller than 10 µm. The NEQ provides estimates in terms of particle number concentration accompanied by a 95% confidence interval (CI), enabling a comprehensive assessment of MN exposure. The NEQ includes 2 quantitative models: a simplified tier 1 model and a more comprehensive tier 2 model. Both tier 1 and tier 2 models exhibit robust performance, with correlation coefficients (r) of 0.57 and 0.62, respectively. The models exhibit a moderate level of error, as indicated by residuals' standard deviation of 4.10 for tier 1 and 3.90 for tier 2. The tier 1 model demonstrates a slightly higher overestimation bias (1.15) compared to the tier 2 model (0.54). Overall, the NEQ offers a practical and reliable approach for estimating MN exposure in occupational settings. Future validation studies will investigate the impact of initial calibration efforts, heteroscedasticity, and further refine the model's accuracy.
期刊介绍：
About the Journal
Annals of Work Exposures and Health is dedicated to presenting advances in exposure science supporting the recognition, quantification, and control of exposures at work, and epidemiological studies on their effects on human health and well-being. A key question we apply to submission is, "Is this paper going to help readers better understand, quantify, and control conditions at work that adversely or positively affect health and well-being?"
We are interested in high quality scientific research addressing:
the quantification of work exposures, including chemical, biological, physical, biomechanical, and psychosocial, and the elements of work organization giving rise to such exposures;
the relationship between these exposures and the acute and chronic health consequences for those exposed and their families and communities;
populations at special risk of work-related exposures including women, under-represented minorities, immigrants, and other vulnerable groups such as temporary, contingent and informal sector workers;
the effectiveness of interventions addressing exposure and risk including production technologies, work process engineering, and personal protective systems;
policies and management approaches to reduce risk and improve health and well-being among workers, their families or communities;
methodologies and mechanisms that underlie the quantification and/or control of exposure and risk.
There is heavy pressure on space in the journal, and the above interests mean that we do not usually publish papers that simply report local conditions without generalizable results. We are also unlikely to publish reports on human health and well-being without information on the work exposure characteristics giving rise to the effects. We particularly welcome contributions from scientists based in, or addressing conditions in, developing economies that fall within the above scope.
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