Pulmonary oxidative stress in wild bats exposed to coal dust: A model to evaluate the impact of coal mining on health

Pedroso-Fidelis, Giulia dos Santos; Farias, Hémelin Resende; Mastella, Gustavo Antunes; Boufleur-Niekraszewicz, Liana Appel; Dias, Johnny Ferraz; Alves, Márcio Correa; Nesi, Renata Tiscoski; Carvalho, Fernando; Zocche, Jairo José; Pinho, Ricardo A.

doi:10.1016/j.ecoenv.2020.110211

Cited by 17 publications

(7 citation statements)

References 85 publications

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“…In order to make the final model perform well in the task of fire-smoke detection in coal mine, we selected the WildfireSmokeDataset from EXTREM MART, which includes 737 labeled wild fire-smoke images. Because the smoke targets in the wild are small and the wild environment is similar to coal mine [ 37 ]. In addition, we also select the open_fire dataset from Github to make the fire and smoke scenarios more diverse, using 1,446 labeled fire-smoke images from it.…”

Section: Methodsmentioning

confidence: 99%

Fire and smoke real-time detection algorithm for coal mines based on improved YOLOv8s

Kong,

Li,

Duan

2024

PLoS ONE

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Fire and smoke detection is crucial for the safe mining of coal energy, but previous fire-smoke detection models did not strike a perfect balance between complexity and accuracy, which makes it difficult to deploy efficient fire-smoke detection in coal mines with limited computational resources. Therefore, we improve the current advanced object detection model YOLOv8s based on two core ideas: (1) we reduce the model computational complexity and ensure real-time detection by applying faster convolutions to the backbone and neck parts; (2) to strengthen the model’s detection accuracy, we integrate attention mechanisms into both the backbone and head components. In addition, we improve the model’s generalization capacity by augmenting the data. Our method has 23.0% and 26.4% fewer parameters and FLOPs (Floating-Point Operations) than YOLOv8s, which means that we have effectively reduced the computational complexity. Our model also achieves a mAP (mean Average Precision) of 91.0%, which is 2.5% higher than the baseline model. These results show that our method can improve the detection accuracy while reducing complexity, making it more suitable for real-time fire-smoke detection in resource-constrained environments.

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Section: Methodsmentioning

confidence: 99%

Fire and smoke real-time detection algorithm for coal mines based on improved YOLOv8s

Kong,

Li,

Duan

2024

PLoS ONE

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“…Среди мелких частиц, образующихся при добыче полезных ископаемых открытым способом, частицы, образующиеся на угольных разрезах, считаются наиболее опасными из-за их различных размеров, химического и минералогического состава (в частности, уголь, минералы, органические соединения и т.д.) [6]. На угольных разрезах многие виды работ могут производить пыль (т.е.…”

Section: Introductionunclassified

Forecasting PM<sub>2.5</sub> emissions in open-pit minesusing a functional link neural network optimized by various optimization algorithms

Bui

Nguyen

et al. 2022

Min.Sci.Technol. (RUS)

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PM2.5 air pollution is not only a significant hazard to human health in everyday life but also a dangerous risk to workers operating in open-pit mines OPMs), especially open-pit coal mines (OPCMs). PM2.5 in OPCMs can cause lung-related (e.g., pneumoconiosis, lung cancer) and cardiovascular diseases due to exposure to airborne respirable dust over a long time. Therefore, the precise prediction of PM2.5 is of great importance in the mitigation of PM2.5 pollution and improving air quality at the workplace. This study investigated the meteorological conditions and PM2.5 emissions at an OPCM in Vietnam, in order to develop a novel intelligent model to predict PM2.5 emissions and pollution. We applied functional link neural network (FLNN) to predict PM2.5 pollution based on meteorological conditions (e.g., temperature, humidity, atmospheric pressure, wind direction and speed). Instead of using traditional algorithms, the Hunger Games Search (HGS) algorithm was used to train the FLNN model. The vital role of HGS in this study is to optimize the weights in the FLNN model, which was finally referred to as the HGS-FLNN model. We also considered three other hybrid models based on FLNN and metaheuristic algorithms, i.e., ABC (Artificial Bee Colony)-FLNN, GA (Genetic Algorithm)- FLNN, and PSO (Particle Swarm Optimization)-FLNN to assess the feasibility of PM2.5 prediction in OPCMs and compare their results with those of the HGS-FLNN model. The study findings showed that HGS-FLNN was the best model with the highest accuracy (up to 94–95 % in average) to predict PM2.5 air pollution. Meanwhile, the accuracy of the other models ranged 87 % to 90 % only. The obtained results also indicated that HGS-FLNN was the most stable model with the lowest relative error (in the range of −0.3 to 0.5 %).

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“…The increase in social productivity has also caused major dust pollution problems that have affected the environment. For example, a significant increase in dust production during industrial coal mining or mechanization of materials can lead to the frequent occurrence of pneumoconiosis in workers. − More than 2.65 million coal miners work within dusty atmospheres in China, and more than 6000 workers die of pneumoconiosis every year. , Besides, road dust produced during mass transportation of minerals and residues can cause reduced visibility, which threatens road safety, and can lead to decreased production and transportation capability. − The growth of the open mine yard and industrial waste yard are hazardous to the air quality in nearby cities, as well as to the health of residents. − The above problems are directly or indirectly caused by a large amount of fugitive dust. Therefore, dust control is of paramount significance to ensure the safety of workers, the health of urban residents, and the sustainable development of mineral resources.…”

Section: Introductionmentioning

confidence: 99%

Preparation of Mussel-Inspired Stable-Bonding Dust Binders for Fugitive Dust Control

Shao

Cheng

et al. 2022

ACS Appl. Polym. Mater.

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Considerable fugitive dust generated during the mining, transportation, and storage of mineral materials pollutes the environment and poses threat to the health of workers and surrounding residents. Spraying polymer solutions is usually used to suppress fugitive dust, but the dust will return to the air under the erosion of wind and rain due to the lack of a stable bonding mechanism with the dust. Based on this, through polymerization of mussel-inspired lignin-Ag nanoparticles (NPs), acrylic acid, and alkyl polyglucoside, an environmentally friendly stable-bonding dust binder was prepared. Different from the high initial viscosity of traditional polymer dust suppressants, the stable-bonding dust binder with high consolidation ability but easily atomized characteristic related to the synthesis of short-chain polymer systems with concentration-sensitive self-assembly behavior. When a 10 m/s wind was applied to dust treated by the stable-bonding dust binder, the mass-loss rate (6%) was lower than the dust treated by only a surfactant solution (100%) or polyacrylic acid solution (27%). When 12 L of water was sprayed on the dust (100 g) treated with the stable-bonding dust binder, the residual stress strength (1 MPa) was higher than the dust treated with only the polyacrylic acid solution (0.017 MPa). Furthermore, molecular dynamics simulation of the interaction between the dust-binder molecules and coal molecules was carried out to confirm the adhesion of lignin-Ag NPs to dust. Our research on dust binders provides a path for the stable control of fugitive dust, which improves the consolidation efficiency of dust and reduces costs.

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Pulmonary oxidative stress in wild bats exposed to coal dust: A model to evaluate the impact of coal mining on health

Cited by 17 publications

References 85 publications

Fire and smoke real-time detection algorithm for coal mines based on improved YOLOv8s

Fire and smoke real-time detection algorithm for coal mines based on improved YOLOv8s

Forecasting PM<sub>2.5</sub> emissions in open-pit minesusing a functional link neural network optimized by various optimization algorithms

Preparation of Mussel-Inspired Stable-Bonding Dust Binders for Fugitive Dust Control

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