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Occupational exposure limits for respirable coal dust are based on exposure during working hours, but coal miners may experience additional community-based exposures during nonworking hours. We analyzed Australia National Pollutant Inventory (NPI) data for the years 2008&ndash, 2018 to estimate air pollutants (metals, nitrogen oxides, particulate matter &le, 10 micrometers (PM10) and &le, 2.5 micrometers (PM2.5)) originating from coal mines. PM10 levels from community-based air monitors in Queensland and New South Wales were also compared between mining and nonmining communities. Results indicated that tons of coal mined increased over the study period, and that levels of particulate matter, metals, and nitrogen oxides increased significantly over time as well. Coal mines accounted for 42.1% of national PM10 air emissions from NPI sites. PM2.5 from coal mines accounted for 19.5% of the national total, metals for 12.1%, and nitrogen oxides for 10.1%. Coal mining occurred in 57 different post codes, the 20 coal-mining post codes with the highest PM10 emissions were home to 160,037 people. Emissions of all studied pollutants were significantly higher from coal mining sites than from other types of NPI sites. Results from community-based air monitoring stations indicated significantly higher population PM10 exposure in coal mining communities than in nonmining communities. The health of the public at large is impacted by coal mining, but to the extent that miners also live near coal mining operations, their total exposure is underestimated by consideration of exposure only during working hours.

Airflow and particle transport in the human lung system is influenced by biological and other factors such as breathing pattern, particle properties, and deposition mechanisms. Most of the studies to date have analyzed airflow characterization and aerosol transport in idealized and realistic models. Precise airflow characterization for airway stenosis in a digital reference model is lacking in the literature. This study presents a numerical simulation of airflow and particle transport through a stenosis section of the airway. A realistic CT-scan-based mouth&ndash, throat and upper airway model was used for the numerical calculations. Three different models of a healthy lung and of airway stenosis of the left and right lung were used for the calculations. The ANSYS FLUENT solver, based on the finite volume discretization technique, was used as a numerical tool. Proper grid refinement and validation were performed. The numerical results show a complex-velocity flow field for airway stenosis, where airflow velocity magnitude at the stenosis section was found to be higher than that in healthy airways. Pressure drops at the mouth&ndash, throat and in the upper airways show a nonlinear trend. Comprehensive pressure analysis of stenosis airways would increase our knowledge of the safe mechanical ventilation of the lung. The turbulence intensities at the stenosis sections of the right and left lung were found to be different. Deposition efficiency (DE) increased with flow rate and particle size. The findings of the present study increase our understanding of airflow patterns in airway stenosis under various disease conditions. More comprehensive stenosis analysis is required to further improve knowledge of the field.

The knowledge of respiratory particle transport in the extra-thoracic pathways is essential for the estimation of lung health-risk and optimization of targeted drug delivery. The published literature reports that a significant fraction of the inhaled aerosol particles are deposited in the upper airways, and available inhalers can deliver only a small amount of drug particles to the deeper airways. To improve the targeted drug delivery efficiency to the lungs, it is important to reduce the drug particle deposition in the upper airways. This study aims to minimize the unwanted aerosol particle deposition in the upper airways by employing a gas mixture model for the aerosol particle transport within the upper airways. A helium&ndash, oxygen (heliox) mixture (80% helium and 20% oxygen) model is developed for the airflow and particle transport as the heliox mixture is less dense than air. The mouth&ndash, throat and upper airway geometry are extracted from CT-scan images. Finite volume based ANSYS Fluent (19.2) solver is used to simulate the airflow and particle transport in the upper airways. Tecplot software and MATLAB code are employed for the airflow and particle post-processing. The simulation results show that turbulence intensity for heliox breathing is lower than in the case of air-breathing. The less turbulent heliox breathing eventually reduces the deposition efficiency (DE) at the upper airways than the air-breathing. The present study, along with additional patient-specific investigation, could improve the understanding of particle transport in upper airways, which may also increase the efficiency of aerosol drug delivery.