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<p>Workers in dry-cleaning workplaces are chronically exposed to hazardous chlorinated solvents, mainly to perchloroethylene (PCE) and trichloroethylene (TCE), which pose significant risks to human health and environmental ecosystems. Accurate assessment of occupational exposure to these volatile compounds is crucial for effective prevention strategies. This investigation applies chemometric methods; combining statistical modeling and unsupervised learning; to predict chemical risk in dry-cleaning facilities in Sfax, the second Tunisian largest and most industrialized city. Data were collected from 47 dry-cleaning sites and included air monitoring of PCE and TCE. Working spaces and sociodemographic characteristics, as well as urinary biomonitoring data for trichloroacetic acid (TA) and trichloroethylene (TE) were obtained from a subset of exposed workers. Multivariate exploratory methods (principal component analysis, multiple correspondence analysis and clustering) were applied to characterize exposure profiles, followed by multiple linear regression aiming to develop predictive models of urinary solvent metabolites. Key predictive variables included atmospheric solvent concentrations, worker gender, and utilization of protective tools. The analyses revealed coherent exposure patterns correlating atmospheric solvent concentrations with internal biomarkers, while individual factors such as gender, and use of protective tools and clothing also contributed to the observed variability. The models demonstrated good predictive performance within the studied sample and support the idea of integrating environmental monitoring, biomonitoring, and multivariate analysis for chemical risk assessment in dry-cleaning facilities. As well, the findings underscore the utility of chemometric tools in occupational risk assessment and highlight the need for tailored preventive measures in small-scale industrial settings. However, additional studies involving larger samples and other regions are needed to confirm and extend these observations.</p>

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Chemometric modeling for occupational exposure to chlorinated solvents in dry-cleaning facilities in Sfax (Tunisia)

  • 期刊论文

  • 发表日期：

    2026年2月18日

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  • Hakim Zina,
  • Belmabrouk Sabrine,
  • Khadhraoui Moncef,
  • Omrane Fatma,
  • Gargouri Imed

摘要

Workers in dry-cleaning workplaces are chronically exposed to hazardous chlorinated solvents, mainly to perchloroethylene (PCE) and trichloroethylene (TCE), which pose significant risks to human health and environmental ecosystems. Accurate assessment of occupational exposure to these volatile compounds is crucial for effective prevention strategies. This investigation applies chemometric methods; combining statistical modeling and unsupervised learning; to predict chemical risk in dry-cleaning facilities in Sfax, the second Tunisian largest and most industrialized city. Data were collected from 47 dry-cleaning sites and included air monitoring of PCE and TCE. Working spaces and sociodemographic characteristics, as well as urinary biomonitoring data for trichloroacetic acid (TA) and trichloroethylene (TE) were obtained from a subset of exposed workers. Multivariate exploratory methods (principal component analysis, multiple correspondence analysis and clustering) were applied to characterize exposure profiles, followed by multiple linear regression aiming to develop predictive models of urinary solvent metabolites. Key predictive variables included atmospheric solvent concentrations, worker gender, and utilization of protective tools. The analyses revealed coherent exposure patterns correlating atmospheric solvent concentrations with internal biomarkers, while individual factors such as gender, and use of protective tools and clothing also contributed to the observed variability. The models demonstrated good predictive performance within the studied sample and support the idea of integrating environmental monitoring, biomonitoring, and multivariate analysis for chemical risk assessment in dry-cleaning facilities. As well, the findings underscore the utility of chemometric tools in occupational risk assessment and highlight the need for tailored preventive measures in small-scale industrial settings. However, additional studies involving larger samples and other regions are needed to confirm and extend these observations.