Exposure assessment of elemental carbon, polycyclic aromatic hydrocarbons and crystalline silica at the underground excavation sites for top-down construction buildings

Differential pulmonary toxicity and autoantibody formation in genetically distinct mouse strains following combined exposure to silica and diesel exhaust particles

BACKGROUND

Inhalation of airborne particulate matter, such as silica and diesel exhaust particles, poses serious long-term respiratory and systemic health risks. Silica exposure can lead to silicosis and systemic autoimmune diseases, while DEP exposure is linked to asthma and cancer. Combined exposure to silica and DEP, common in mining, may have more severe effects. This study investigates the separate and combined effects of occupational-level silica and ambient-level DEP on lung injury, inflammation, and autoantibody formation in two genetically distinct mouse strains, thereby aiming at understanding the interplay between genetic susceptibility, particulate exposure, and disease outcomes. Silica and diesel exhaust particles were administered to mice via oropharyngeal aspiration. Assessments of lung injury and host response included in vivo lung micro-computed tomography, lung function tests, bronchoalveolar lavage fluid analysis including inflammatory cytokines and antinuclear antibodies, and histopathology with particle colocalization.

RESULTS

The findings highlight the distinct effects of silica and diesel exhaust particles (DEP) on lung injury, inflammation, and autoantibody formation in C57BL/6J and NOD/ShiLtJ mice. Silica exposure elicited a well-established inflammatory response marked by inflammatory infiltrates, release of cytokines, and chemokines, alongside mild fibrosis, indicated by collagen deposition in the lungs of both C57BL/6J and NOD/ShilLtJ mice. Notably, these strains exhibited divergent responses in terms of respiratory function and lung volumes, as assessed through micro-computed tomography. Additionally, silica exposure induced airway hyperreactivity and elevated antinuclear antibody levels in bronchoalveolar lavage fluid, particularly prominent in NOD/ShiLtJ mice. Moreover, antinuclear antibodies correlated with extent of lung inflammation in NOD/ShiLTJ mice. Lung tissue analysis revealed DEP loaded macrophages and co-localization of silica and DEP particles. However, aside from contributing to airway hyperreactivity specifically in NOD/ShiLtJ mice, the ambient-level DEP did not significantly amplify the effects induced by silica. There was no evidence of synergistic or additive interaction between these specific doses of silica and DEP in inducing lung damage or inflammation in either of the mouse strains.

CONCLUSION

Mouse strain variations exerted a substantial influence on the development of silica induced lung alterations. Furthermore, the additional impact of ambient-level DEP on these silica-induced effects was minimal.

Differential Pulmonary Toxicity and Autoantibody Formation in Genetically Distinct Mouse Strains Following Combined Exposure to Silica and Diesel Exhaust Particles

Background

Inhalation of airborne particulate matter, such as silica and diesel exhaust particles, poses serious long-term respiratory health risks. Silica exposure can lead to silicosis and systemic autoimmune diseases, while DEP exposure is linked to asthma and cancer. Combined exposure to silica and DEP, common in mining, may have more severe effects. This study investigates the separate and combined effects of silica and DEP on lung injury, inflammation, and autoantibody formation in two genetically distinct mouse strains, thereby aiming at understanding the interplay between genetic susceptibility, particulate exposure, and disease outcomes. Silica and diesel exhaust particles were administered to mice via oropharyngeal aspiration. Assessments of lung injury and host response included in vivo lung micro-computed tomography, lung function tests, bronchoalveolar lavage fluid analysis including inflammatory cytokines and antinuclear antibodies, and histopathology with particle colocalization.

Results

Silica exposure elicited a well-established inflammatory response marked by inflammatory infiltrates, release of cytokines, and chemokines, alongside limited fibrosis, indicated by collagen deposition in the lungs of both C57BL/6J and NOD/ShilLtJ mice. Notably, these strains exhibited divergent responses in terms of respiratory function and lung volumes, as assessed through micro-computed tomography. Additionally, silica exposure induced airway hyperreactivity and elevated antinuclear antibody levels in bronchoalveolar lavage fluid, particularly prominent in NOD/ShiLtJ mice. Lung tissue analysis revealed DEP loaded macrophages and co-localization of silica and DEP particles.

Conclusion

Mouse strain variations exerted a substantial influence on the development of silica induced lung alterations. Furthermore, the additional impact of diesel exhaust particles on these silica-induced effects was minimal.

Green buildings model: Impact of rigid polyurethane foam on indoor environment and sustainable development in energy sector

The construction and building industry in the modern world heavily relies on advanced techniques and materials such as polymers. However, with the world's population alarmingly increasing, contributing to the greenhouse effect, and severe weather conditions amplifying, it has become crucial to reduce the heat effects in both new and old buildings. To achieve this, 50-70% more energy is necessary, which highlights the importance of energy-efficient construction practices and materials. Consequently, a comprehensive study was conducted to evaluate the efficacy of Polyurethane in indoor environments and energy conservation. Current study was performed due to an innovative application of insulation materials as to reduce the heat and energy costs in construction works. Thermal conductivity at mean temperature 35 °C was found 0.0272 (W/m K) with maximum in burnt clay brick (1.43 W/m K) by using hotplate apparatus. Specific heat was also found less 0.85 (KJ/Kg K) at density 32 kg/m³ while results were at par in reinforcement cement concrete and burnt clay brick 0.91, 0.91 (KJ/Kg K) respectively. Similarly, heat transmittance values of different roof sections by using polyurethane insulation showing satisfaction the ECBC in Buildings deviating standard U-value 1.20% to 0.418 (W/m² K) with its excellent performance. Polyurethane treatments have been found to exert a significant impact on the computation of thermal resistance and overall heat transfer coefficients. In contrast, non-insulated treatments yielded inconclusive results with little to no significance. This highlights the importance of insulation materials in energy-efficient construction practices. Energy consumption in winter and summer also has shown the significant impact by using polyurethane application with cumulative saving of 60-62% electricity. Economic Benefit of polyurethane in RCC and Conventional buildings describes positive and highly significant impact in present study. Application of polyurethane in new and old buildings ultimate enhanced the better quality of life and living standards from people of applied countries and is strongly recommended for future prospects and endeavors as Eco-friendly and energy efficient for sustainable development.

Analyzing point cloud of coal mining process in much dust environment based on dynamic graph convolution neural network

Environmental perception is an important research direction of coal mine sustainable development. There is much dust in the underground working environment of coal mine. This study is to identify the marker (ball) in the coal mine, which provides a basic to convert the coordinate of large-scale fully mechanized mining face point cloud to the geodetic coordinate. Firstly, in the face of the phenomenon that the uneven distribution of underground point cloud is more serious, this study further has studied on the basis of complete and incomplete geometry point cloud and generated multi-density geometry point cloud for the first time. Secondly, aiming at the problem that the geometric features of underground point cloud are not obvious enough, this study has increased the weight of point cloud normal vector in the training process of network model, so that the network model is more sensitive to different geometric features. Finally, this study has used a variety of advanced deep neural networks to directly analyze point clouds to verify the proposed method. The results show that the method proposed in this study has been combined with the dynamic graph convolution neural network (DGCNN) established earlier, which can more accurately identify the ball in tens of millions of the point clouds of coal mining process. Most importantly, this work is not only of great significance to improve the production efficiency and safety in fully mechanized mining face but also lays a foundation for realizing intelligence in the mining field and avoiding the harm of dust explosion and other accidents to workers.

Diesel Engine Exhaust Exposure in the Ontario Civil Infrastructure Construction Industry

OBJECTIVES

Diesel engine exhaust (DEE) is a known lung carcinogen and a common occupational exposure in Canada. The use of diesel-powered equipment in the construction industry is particularly widespread, but little is known about DEE exposures in this work setting. The objective of this study was to determine exposure levels and identify and characterize key determinants of DEE exposure at construction sites in Ontario.

METHODS

Elemental carbon (EC, a surrogate of DEE exposure) measurements were collected at seven civil infrastructure construction worksites and one trades training facility in Ontario using NIOSH method 5040. Full-shift personal air samples were collected using a constant-flow pump and SKC aluminium cyclone with quartz fibre filters in a 37-mm cassette. Exposures were compared with published health-based limits, including the Dutch Expert Committee on Occupational Safety (DECOS) limit (1.03 µg m-3 respirable EC) and the Finnish Institute of Occupational Health (FIOH) recommendation (5 µg m-3 respirable EC). Mixed-effects linear regression was used to identify determinants of EC exposure.

RESULTS

In total, 149 EC samples were collected, ranging from <0.25 to 52.58 µg m-3 with a geometric mean (GM) of 3.71 µg m-3 [geometric standard deviation (GSD) = 3.32]. Overall, 41.6% of samples exceeded the FIOH limit, mostly within underground worksites (93.5%), and 90.6% exceeded the DECOS limit. Underground workers (GM = 13.20 µg m-3, GSD = 1.83) had exposures approximately four times higher than below grade workers (GM = 3.56 µg m-3, GSD = 1.94) and nine times higher than above ground workers (GM = 1.49 µg m-3, GSD = 1.75). Training facility exposures were similar to above ground workers (GM = 1.86 µg m-3, GSD = 4.12); however, exposures were highly variable. Work setting and enclosed cabins were identified as the key determinants of exposure in the final model (adjusted R2 = 0.72, P < 0.001). The highest DEE exposures were observed in underground workplaces and when using unenclosed cabins.

CONCLUSIONS

This study provides data on current DEE exposure in Canadian construction workers. Most exposures were above recommended health-based limits, albeit in other jurisdictions, signifying a need to further reduce DEE levels in construction. These results can inform a hazard reduction strategy including targeted intervention/control measures to reduce DEE exposure and the burden of occupational lung cancer.