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Occupational Respiratory Health: Second Edition
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Occupational Respiratory Health: Second Edition

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Occupational Safety and Health".

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 18505

Special Issue Editors

Dr. Suvash C. Saha

E-Mail Website
Guest Editor
School of Mechanical and Mechatronic Engineering, Faculty of Engineering & Information Technology, University of Technology Sydney, Broadway, NSW 2007, Australia
Interests: solar thermal energy technology; heat transfer in buildings; computational fluid dynamics; boundary layer theory; transport in porous media; magnetic convection; modeling of particle deposition, clearance, and interaction with lung surfactant; numerical modeling of deformation issue of RBCs related to their aging
Special Issues, Collections and Topics in MDPI journals
Dr. Saidul Islam

E-Mail Website
Guest Editor
School of Mechanical and Mechatronic Engineering, University of Technology Sydney (UTS), 15 Broadway, Ultimo, NSW 2007, Australia
Interests: heat wave; bush fire and air-quality; particle transport, and deposition; human lung modelling; microfluidics; CFD; heat and mass transfer
Special Issues, Collections and Topics in MDPI journals
Dr. Gunther Paul

E-Mail Website
Guest Editor
Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, QLD 4810, Australia
Interests: occupational health and safety; ergonomics; biomechanics; public health; human system interface; rehabilitation; respiratory health
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to the success of the first edition of the Special Issue “Occupational Respiratory Health” published in the International Journal of Environmental Research and Public Health (IJERPH) (https://www.mdpi.com/journal/ijerph/special_issues/respiratory_health), we would like to continue exploration of this topic. IJERPH is a peer-reviewed scientific journal that publishes articles and communications in the interdisciplinary area of environmental health sciences and public health. For detailed information about the journal, please visit: https://www.mdpi.com/journal/ijerph. 

Improving respiratory health is an important objective for policy makers associated with the mining, construction and processing industries. Respiratory health refers to preventing disease of the respiratory tract, and prolonging the life of those exposed to excessive levels of respiratory hazards, promoting health and well-being. Occupational environments, including above ground and underground mines, construction sites, tunnelling sites, power plants, workshops, farms, processing plants and transportation systems, may affect the respiratory health of workers and the public, through individual exposure levels (e.g., dust particle exposure), activity patterns (e.g., heavy physical work), pre-existing medical conditions, and individual resilience. Research in a variety of contexts—indoor workplaces and open areas, developing and developed countries—can offer a critical guide for policy efforts and planning for occupational and environmental health, and inform proactivity to improve productivity.

This second edition of the Special Issue is still open to any subject area related to the impact of particle emissions on occupational respiratory health. The listed keywords suggest just a few of the many possibilities.

Dr. Suvash C. Saha
Dr. Saidul Islam
Prof. Dr. Gunther Paul
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Underground mining
  • Open-cut mining
  • Engineered stone processing
  • Construction
  • Tunnelling
  • Bushfire
  • Air quality
  • Farming
  • Food processing
  • Public health
  • Occupational health
  • Occupational lung disease
  • Environmental health
  • Environmental exposure
  • Particle exposure
  • Particle emission
  • Crystalline Silica emission
  • Coal Mine Dust Lung Disease (CMDLD)
  • Black lung
  • SARS CoV-2 virus transport
  • Coal Worker Pneumoconiosis (CWP)
  • Progressive Massive Fibrosis (PMF)
  • Silicosis
  • Computational Fluid Dynamics (CFD)
  • Multiphase flow
  • Chronic Obstructive Pulmonary Disease (COPD)
  • Asbestosis
  • Emphysema
  • Mathematical modelling
  • Virus transport
  • Occupational asthma
  • Ventilation
  • Water spraying
  • Personal Dust Monitor (PDM)
  • Real time monitoring
  • Dust purification
  • Personal Protective Equipment (PPE)
  • Air purification
  • Biomedical modelling

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

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21 pages, 8096 KiB  
Article
Detection and Visualisation of Pneumoconiosis Using an Ensemble of Multi-Dimensional Deep Features Learned from Chest X-rays
by Liton Devnath, Zongwen Fan, Suhuai Luo, Peter Summons and Dadong Wang
Int. J. Environ. Res. Public Health 2022, 19(18), 11193; https://doi.org/10.3390/ijerph191811193 - 6 Sep 2022
Cited by 16 | Viewed by 2560
Abstract
Pneumoconiosis is a group of occupational lung diseases induced by mineral dust inhalation and subsequent lung tissue reactions. It can eventually cause irreparable lung damage, as well as gradual and permanent physical impairments. It has affected millions of workers in hazardous industries throughout [...] Read more.
Pneumoconiosis is a group of occupational lung diseases induced by mineral dust inhalation and subsequent lung tissue reactions. It can eventually cause irreparable lung damage, as well as gradual and permanent physical impairments. It has affected millions of workers in hazardous industries throughout the world, and it is a leading cause of occupational death. It is difficult to diagnose early pneumoconiosis because of the low sensitivity of chest radiographs, the wide variation in interpretation between and among readers, and the scarcity of B-readers, which all add to the difficulty in diagnosing these occupational illnesses. In recent years, deep machine learning algorithms have been extremely successful at classifying and localising abnormality of medical images. In this study, we proposed an ensemble learning approach to improve pneumoconiosis detection in chest X-rays (CXRs) using nine machine learning classifiers and multi-dimensional deep features extracted using CheXNet-121 architecture. There were eight evaluation metrics utilised for each high-level feature set of the associated cross-validation datasets in order to compare the ensemble performance and state-of-the-art techniques from the literature that used the same cross-validation datasets. It is observed that integrated ensemble learning exhibits promising results (92.68% accuracy, 85.66% Matthews correlation coefficient (MCC), and 0.9302 area under the precision–recall (PR) curve), compared to individual CheXNet-121 and other state-of-the-art techniques. Finally, Grad-CAM was used to visualise the learned behaviour of individual dense blocks within CheXNet-121 and their ensembles into three-color channels of CXRs. We compared the Grad-CAM-indicated ROI to the ground-truth ROI using the intersection of the union (IOU) and average-precision (AP) values for each classifier and their ensemble. Through the visualisation of the Grad-CAM within the blue channel, the average IOU passed more than 90% of the pneumoconiosis detection in chest radiographs. Full article
(This article belongs to the Special Issue Occupational Respiratory Health: Second Edition)
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Review

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22 pages, 4637 KiB  
Review
Computer-Aided Diagnosis of Coal Workers’ Pneumoconiosis in Chest X-ray Radiographs Using Machine Learning: A Systematic Literature Review
by Liton Devnath, Peter Summons, Suhuai Luo, Dadong Wang, Kamran Shaukat, Ibrahim A. Hameed and Hanan Aljuaid
Int. J. Environ. Res. Public Health 2022, 19(11), 6439; https://doi.org/10.3390/ijerph19116439 - 25 May 2022
Cited by 29 | Viewed by 7121
Abstract
Computer-aided diagnostic (CAD) systems can assist radiologists in detecting coal workers’ pneumoconiosis (CWP) in their chest X-rays. Early diagnosis of the CWP can significantly improve workers’ survival rate. The development of the CAD systems will reduce risk in the workplace and improve the [...] Read more.
Computer-aided diagnostic (CAD) systems can assist radiologists in detecting coal workers’ pneumoconiosis (CWP) in their chest X-rays. Early diagnosis of the CWP can significantly improve workers’ survival rate. The development of the CAD systems will reduce risk in the workplace and improve the quality of chest screening for CWP diseases. This systematic literature review (SLR) amis to categorise and summarise the feature extraction and detection approaches of computer-based analysis in CWP using chest X-ray radiographs (CXR). We conducted the SLR method through 11 databases that focus on science, engineering, medicine, health, and clinical studies. The proposed SLR identified and compared 40 articles from the last 5 decades, covering three main categories of computer-based CWP detection: classical handcrafted features-based image analysis, traditional machine learning, and deep learning-based methods. Limitations of this review and future improvement of the review are also discussed. Full article
(This article belongs to the Special Issue Occupational Respiratory Health: Second Edition)
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25 pages, 6935 KiB  
Review
Is the SARS CoV-2 Omicron Variant Deadlier and More Transmissible Than Delta Variant?
by Bao V. Duong, Puchanee Larpruenrudee, Tianxin Fang, Sheikh I. Hossain, Suvash C. Saha, Yuantong Gu and Mohammad S. Islam
Int. J. Environ. Res. Public Health 2022, 19(8), 4586; https://doi.org/10.3390/ijerph19084586 - 11 Apr 2022
Cited by 72 | Viewed by 7389
Abstract
Genetic variants of severe acute respiratory syndrome coronavirus (SARS-CoV-2) have been globally surging and devastating many countries around the world. There are at least eleven reported variants dedicated with inevitably catastrophic consequences. In 2021, the most dominant Delta and Omicron variants were estimated [...] Read more.
Genetic variants of severe acute respiratory syndrome coronavirus (SARS-CoV-2) have been globally surging and devastating many countries around the world. There are at least eleven reported variants dedicated with inevitably catastrophic consequences. In 2021, the most dominant Delta and Omicron variants were estimated to lead to more severity and deaths than other variants. Furthermore, these variants have some contagious characteristics involving high transmissibility, more severe illness, and an increased mortality rate. All outbreaks caused by the Delta variant have been rapidly skyrocketing in infection cases in communities despite tough restrictions in 2021. Apart from it, the United States, the United Kingdom and other high-rate vaccination rollout countries are still wrestling with this trend because the Delta variant can result in a significant number of breakthrough infections. However, the pandemic has changed since the latest SARS-CoV-2 variant in late 2021 in South Africa, Omicron. The preliminary data suggest that the Omicron variant possesses 100-fold greater than the Delta variant in transmissibility. Therefore, this paper aims to review these characteristics based on the available meta-data and information from the first emergence to recent days. Australia and the five most affected countries, including the United States, India, Brazil, France, as well as the United Kingdom, are selected in order to review the transmissibility, severity and fatality due to Delta and Omicron variants. Finally, the vaccination programs for each country are also reviewed as the main factor in prevention. Full article
(This article belongs to the Special Issue Occupational Respiratory Health: Second Edition)
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