Health risks of occupational exposure to toxic chemicals in coal mine workplaces based on risk assessment mathematical model based on deep learning

Fan, Zhao; Xu, Fang

doi:10.1016/j.eti.2021.101500

Cited by 17 publications

(5 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Cha et al used a random forest regressor to predict the oxidant exposure during ozonation reactions. Fan and Xu implemented deep learning models for predicting the health risks of occupational exposure to toxic chemicals in coal mine workplaces. ML approaches and techniques have been used for the modeling, design, and prediction of energy systems, including the use of support vector machines to estimate carbon dioxide emissions from combined cycle gas turbine plants .…”

Section: Introductionmentioning

confidence: 99%

Predicting Chemical End-of-Life Scenarios Using Structure-Based Classification Models

Hernández-Betancur

Ruiz-Mercado

Martín

2023

ACS Sustainable Chem. Eng.

View full text Add to dashboard Cite

Analyzing chemicals and their effects on the environment from a life cycle viewpoint can produce a thorough analysis that takes end-of-life (EoL) activities into account. Chemical risk assessment, predicting environmental discharges, and finding EoL paths and exposure scenarios all depend on chemical flow data availability. However, it is challenging to gain access to such data and systematically determine EoL activities and potential chemical exposure scenarios. As a result, this work creates quantitative structure-transfer relationship (QSTR) models for aiding environmental managment decision-making based on chemical structure-based machine learning (ML) models to predict potential industrial EoL activities, chemical flow allocation, environmental releases, and exposure routes. Further multi-label classification methods may improve the predictability of QSTR models according to the ML experiment tracking. The developed QSTR models will assist stakeholders in predicting and comprehending potential EoL management activities and recycling loops, enabling environmental decision-making and EoL exposure assessment for new or existing chemicals in the global marketplace.

show abstract

Section: Introductionmentioning

confidence: 99%

Predicting Chemical End-of-Life Scenarios Using Structure-Based Classification Models

Hernández-Betancur

Ruiz-Mercado

Martín

2023

ACS Sustainable Chem. Eng.

View full text Add to dashboard Cite

show abstract

“…An unsafe act is an action or behaviour that departs from accepted safety standards and may harm an individual or group. Researchers have proven that unsafe acts are caused by two factors: (1) internal factors such as risk tolerance, risk perception, and self-efficacy [ 14 , 15 ], and (2) external factors like safety culture, work environment, and conditions [ [16] , [17] , [18] , [19] , [20] , [21] ].…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, other researchers classified risk as low, medium, high, or extremely high based on the risk rating calculated by the product of consequences, probability, and exposure to the event at the workplace. Authors have also classified risk based on the association with major hazards due to different mining operations like drilling, blasting, transporting, inundation, ventilation, illumination, processing of minerals, fire and coal dust explosion, electrical, and hazards in underground mines like fall of roof, sides, air blast etc [ 18 , [50] , [51] , [52] , [53] , [54] , [55] ]. Risk profiling of any organization is very important activity for identifying the critical groups and treating them and for further suitable deployment of manpower according to risk involved in any activity.…”

Section: Introductionmentioning

confidence: 99%

Reducing workplace unsafe behaviour using risk classification, profiling, risk tolerance approach

Kumar

Bhattacharjee

2023

Heliyon

View full text Add to dashboard Cite

“…In recent years, numerous studies have been conducted to examine the importance of chemical risk assessment in various occupations (Fan and Xu, 2021; Landberg et al, 2018). There are limited studies on health risks due to exposure to BTEX in different shoe industry-related workplaces, especially small shoe workshop units, shoe markets, and shoe stores.…”

Section: Introductionmentioning

confidence: 99%

Health risk assessment of exposure to benzene, toluene, ethyl benzene, and xylene in shoe industry-related workplaces

Abouee-Mehrizi,

Soltanpour,

Mohammadian

et al. 2023

Toxicol Ind Health

View full text Add to dashboard Cite

Benzene, toluene, ethyl benzene, and xylene (BTEX) are prevalent pollutants in shoe industry-related workplaces. The aim of this study was to assess exposure to BTEX and their carcinogenic and non-carcinogenic risks in shoe-industry-related workplaces. This study was carried out at different shoe manufactures, small shoe workshop units, shoe markets, and shoe stores in Tabriz, Iran in 2021. Personal inhalation exposure to BTEX was measured using the National Institute for Occupational Safety and Health (NIOSH) 1501 method. Carcinogenic and non-carcinogenic risks due to inhalation exposure to BTEX were estimated by United States Environmental Protection Agency (U.S. EPA) method based on Mont Carlo simulation. Results showed that the concentrations of benzene and toluene were higher than the threshold limit value (TLV) in both gluing and non-gluing units of shoe manufactures. The total carcinogenic risk (TCR) due to exposure to benzene and ethyl benzene was considerable in all shoe industry-related workplaces. Also, the hazard index (HI) as a non-carcinogenic index was higher than standard levels in all shoe industry-related workplaces. Therefore, shoe industry-related workers are at cancer and non-cancer risks due to exposure to BTEX. Prevention measures need to be implemented to reduce the concentration of BTEX in shoe industry-related workplaces.

show abstract

Health risks of occupational exposure to toxic chemicals in coal mine workplaces based on risk assessment mathematical model based on deep learning

Cited by 17 publications

References 18 publications

Predicting Chemical End-of-Life Scenarios Using Structure-Based Classification Models

Predicting Chemical End-of-Life Scenarios Using Structure-Based Classification Models

Reducing workplace unsafe behaviour using risk classification, profiling, risk tolerance approach

Health risk assessment of exposure to benzene, toluene, ethyl benzene, and xylene in shoe industry-related workplaces

Contact Info

Product

Resources

About