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Scientific Reports Jun 2024The dust pollution caused by the operation of fully mechanized heading face poses a serious threat to the safety production of operators and working face. To reduce dust...
The dust pollution caused by the operation of fully mechanized heading face poses a serious threat to the safety production of operators and working face. To reduce dust concentration at the fully mechanized heading face, this study analyzed dust samples collected from various positions to understand the particle size distribution characteristics. Based on these findings, a conical diversion air conditioning (CDAC) device was designed to create a radial air curtain for dust control in the roadway cross-section. Computational Fluid Dynamics (CFD) was then employed to investigate the airflow and particle dynamics when the cone-shaped deflector was in closed and open states. The results show that in the fully mechanized heading face, the dust distribution in the working area of the roadheader driver is relatively dense, and the dust particles with particle size ≤ 8 μm account for a large proportion. When the CDAC device is deployed, the axial airflow in the roadway is changed into a rotating airflow along the roadway wall, and an air screen is established in the working area of the roadheader driver to block the outward diffusion of dust. When the pressure air outlet is arranged 30 m away from the tunneling head, the pressure air volume is set to 400 m/min, and the CDAC device can better form the air curtain barrier to block the dust particles. It provides a new method for effectively controlling the dust concentration of the fully mechanized heading face and directly ensuring the health of the roadheader driver.
PubMed: 38844509
DOI: 10.1038/s41598-024-63881-4 -
Jamba (Potchefstroom, South Africa) 2024This study aimed to compare how vulnerable informal and formal households are to disaster risks in Bekkersdal mining area in the Rand West City municipality, using a...
UNLABELLED
This study aimed to compare how vulnerable informal and formal households are to disaster risks in Bekkersdal mining area in the Rand West City municipality, using a mixed method. A qualitative approach was used to construct a situational analysis of the community, while a questionnaire was used to collect descriptive data. Findings showed both household types (formal and informal) experienced disaster-related risks as their coping resources are limited. Disaster risks such as mining-related incidents (dust, earth tremors and windstorms) are more significant in the informal areas than in formal areas because of their geographical locations. The latter are located in high density areas, with limited access to basic services. Resulting in among others, construction of illegal informal areas and use of illegal electricity connections. These disaster incidents occur against the backdrop of an already vulnerable dolomitic environment that tends to form sinkholes. Thus, the negative impact of mining is the highest disaster risk factor in the area, yet households seem tolerant and distant as they perceive mining as a job opportunity. The study recommended the municipality to include disaster risks in their integrated development plans to ensure that sustainable mining practices are in place to minimise the negative effects in the area. The rehabilitation of mines, measures to prevent informal construction or illegal occupation, and educational awareness on mitigation and adaptation measures are necessary.
CONTRIBUTION
The study adds to the body of knowledge by revising some old techniques of addressing disaster risk measures, especially in surrounding mining communities.
PubMed: 38840978
DOI: 10.4102/jamba.v16i1.1589 -
Scientific Reports Jun 2024Air pollution is a serious environmental health concern for humans and other living organisms. This study analyzes the spatial and temporal characteristics of air...
Air pollution is a serious environmental health concern for humans and other living organisms. This study analyzes the spatial and temporal characteristics of air pollutant concentrations, changes in the degree of pollution, and the wavelet coherence of the air quality index (AQI) with pollutants in various monitoring stations. The analysis is based on long-term time series data (January 2016 to December 2023) of air pollutants (PM, PM and O) from Korla, an oasis city in the northeastern part of the Tarim Basin, China. The concentrations of PM, PM and O in Korla showed a cyclical trend from 2016 to 2023; PM concentrations exhibited all-season exceedance and PM exhibited exceedance only in spring. PM and PM showed a seasonal distribution of spring > winter > fall > summer; O concentrations showed a seasonal distribution of summer > spring > fall > winter. Strong positive wavelet coherence between PM and Air Quality Index (AQI) data series suggests that the AQI data series can effectively characterize fluctuating trends in PM concentrations. Moreover, PM levels IV and VI were maintained at approximately 10%, indicating that sand and dust have a substantial influence on air quality and pose potential threats to the health of urban inhabitants. Based on the results of this study, future efforts must strengthen relative countermeasures for sand prevention and control, select urban greening species with anti-pollution capabilities, rationally expand urban green spaces, and restrict regulations for reducing particulate matter emissions within city areas.
PubMed: 38839810
DOI: 10.1038/s41598-024-63856-5 -
Scientific Reports Jun 2024We previously reported that asthma prevalence was higher in the United States (US) compared to Mexico (MX) (25.8% vs. 8.4%). This investigation assessed differences in...
We previously reported that asthma prevalence was higher in the United States (US) compared to Mexico (MX) (25.8% vs. 8.4%). This investigation assessed differences in microbial dust composition in relation to demographic and housing characteristics on both sides of the US-MX Border. Forty homes were recruited in the US and MX. Home visits collected floor dust and documented occupants' demographics, asthma prevalence, housing structure, and use characteristics. US households were more likely to have inhabitants who reported asthma when compared with MX households (30% vs. 5%) and had significantly different flooring types. The percentage of households on paved roads, with flushing toilets, with piped water and with air conditioning was higher in the US, while dust load was higher in MX. Significant differences exist between countries in the microbial composition of the floor dust. Dust from Mexican homes was enriched with Alishewanella, Paracoccus, Rheinheimera genera and Intrasporangiaceae family. A predictive metagenomics analysis identified 68 significantly differentially abundant functional pathways between US and MX. This study documented multiple structural, environmental, and demographic differences between homes in the US and MX that may contribute to significantly different microbial composition of dust observed in these two countries.
Topics: Dust; Arizona; Humans; Mexico; Housing; Asthma; Bacteria; Female; Family Characteristics; Male; Metagenomics
PubMed: 38834753
DOI: 10.1038/s41598-024-63356-6 -
Environment International Jul 2024Toxicity of particulate matter (PM) depends on its sources, size and composition. We identified PM sources and determined their contribution to oxidative potential (OP)...
Toxicity of particulate matter (PM) depends on its sources, size and composition. We identified PM sources and determined their contribution to oxidative potential (OP) as a health proxy for PM exposure in an Alpine valley influenced by cement industry. PM filter sample chemical analysis and equivalent black carbon (eBC) were measured at an urban background site from November 2020 to November 2021. Using an optimized Positive Matrix Factorization (PMF) model, the source chemical fingerprints and contributions to PM were determined. The OP assessed through two assays, ascorbic acid (AA) and dithiothreitol (DTT), was attributed to the PM sources from the PMF model with a multiple linear regression (MLR) model. Ten factors were found at the site, including biomass burning (34, 40 and 38% contribution to annual PM, OP and OP, respectively), traffic (14, 19 and 7%), nitrate- and sulphate-rich (together: 16, 5 and 8%), aged sea salt (2, 2 and 0%) and mineral dust (10, 12 and 17%). The introduction of innovative organic tracers allowed the quantification of the PM primary and secondary biogenic fractions (together: 13, 8 and 21%). In addition, two unusual factors due to local features, a chloride-rich factor and a second mineral dust-rich factor (named the cement dust factor) were found, contributing together 10, 14 and 8%. We associate these two factors to different processes in the cement plant. Despite their rather low contribution to PM mass, these sources have one of the highest OPs per µg of source. The results of the study provide vital information about the influence of particular sources on PM and OP in complex environments and are thus useful for PM control strategies and actions.
Topics: Particulate Matter; Air Pollutants; Environmental Monitoring; Biomass; Oxidation-Reduction; Vehicle Emissions; Air Pollution
PubMed: 38833875
DOI: 10.1016/j.envint.2024.108787 -
Frontiers in Chemistry 2024IoT-based Sensors networks play a pivotal role in improving air quality monitoring in the Middle East. They provide real-time data, enabling precise tracking of... (Review)
Review
IoT-based Sensors networks play a pivotal role in improving air quality monitoring in the Middle East. They provide real-time data, enabling precise tracking of pollution trends, informed decision-making, and increased public awareness. Air quality and dust pollution in the Middle East region may leads to various health issues, particularly among vulnerable populations. IoT-based Sensors networks help mitigate health risks by offering timely and accurate air quality data. Air pollution affects not only human health but also the region's ecosystems and contributes to climate change. The economic implications of deteriorated air quality include healthcare costs and decreased productivity, underscore the need for effective monitoring and mitigation. IoT-based data can guide policymakers to align with Sustainable Development Goals (SDGs) related to health, clean water, and climate action. The conventional monitor based standard air quality instruments provide limited spatial coverage so there is strong need to continue research integrated with low-cost sensor technologies to make air quality monitoring more accessible, even in resource-constrained regions. IoT-based Sensors networks monitoring helps in understanding these environmental impacts. Among these IoT-based Sensors networks, sensors are of vital importance. With the evolution of sensors technologies, different types of sensors materials are available. Among this carbon based sensors are widely used for air quality monitoring. Carbon nanomaterial-based sensors (CNS) and carbon nanotubes (CNTs) as adsorbents exhibit unique capabilities in the measurement of air pollutants. These sensors are used to detect gaseous pollutants that includes oxides of nitrogen and Sulphur, and ozone, and volatile organic compounds (VOCs). This study provides comprehensive review of integration of carbon nanomaterials based sensors in IoT based network for better air quality monitoring and exploring the potential of machine learning and artificial intelligence for advanced data analysis, pollution source identification, integration of satellite and ground-based networks and future forecasting to design effective mitigation strategies. By prioritizing these recommendations, the Middle East and other regions, can further leverage IoT-based systems to improve air quality monitoring, safeguard public health, protect the environment, and contribute to sustainable development in the region.
PubMed: 38831915
DOI: 10.3389/fchem.2024.1391409 -
Frontiers in Neuroscience 2024Glioblastoma (GBM) is a highly aggressive malignant tumor of the central nervous system that displays varying molecular and morphological profiles, leading to...
INTRODUCTION
Glioblastoma (GBM) is a highly aggressive malignant tumor of the central nervous system that displays varying molecular and morphological profiles, leading to challenging prognostic assessments. Stratifying GBM patients according to overall survival (OS) from H&E-stained whole slide images (WSI) using advanced computational methods is challenging, but with direct clinical implications.
METHODS
This work is focusing on GBM (IDH-wildtype, CNS WHO Gr.4) cases, identified from the TCGA-GBM and TCGA-LGG collections after considering the 2021 WHO classification criteria. The proposed approach starts with patch extraction in each WSI, followed by comprehensive patch-level curation to discard artifactual content, i.e., glass reflections, pen markings, dust on the slide, and tissue tearing. Each patch is then computationally described as a feature vector defined by a pre-trained VGG16 convolutional neural network. Principal component analysis provides a feature representation of reduced dimensionality, further facilitating identification of distinct groups of morphology patterns, via unsupervised k-means clustering.
RESULTS
The optimal number of clusters, according to cluster reproducibility and separability, is automatically determined based on the rand index and silhouette coefficient, respectively. Our proposed approach achieved prognostic stratification accuracy of 83.33% on a multi-institutional independent unseen hold-out test set with sensitivity and specificity of 83.33%.
DISCUSSION
We hypothesize that the quantification of these clusters of morphology patterns, reflect the tumor's spatial heterogeneity and yield prognostic relevant information to distinguish between short and long survivors using a decision tree classifier. The interpretability analysis of the obtained results can contribute to furthering and quantifying our understanding of GBM and potentially improving our diagnostic and prognostic predictions.
PubMed: 38831756
DOI: 10.3389/fnins.2024.1304191 -
Heliyon Jun 2024This paper presents a novel hybrid model employing Artificial Neural Networks (ANN) and Mathematical Morphology (MM) for the effective detection of defects in solar...
This paper presents a novel hybrid model employing Artificial Neural Networks (ANN) and Mathematical Morphology (MM) for the effective detection of defects in solar cells. Focusing on issues such as broken corners and black edges caused by environmental factors like broken glass cover, dust, and temperature variations. This study utilizes a hybrid model of ANN and K-Nearest Neighbor (KNN) for temperature prediction. This hybrid approach leverages the strengths of both models, potentially opening up new avenues for improved accuracy in temperature forecasting, which is critical for solar energy applications. The significance lies in the interconnectedness of temperature fluctuations and solar cell efficiency, leading to defects. The proposed model aims to predict temperatures accurately, providing insights into potential solar cell efficiency problems. Subsequently, this work studies the transitions to defect detection using Fuzzy C-Means (FCM) clustering and MM techniques. The hybrid model demonstrates accurate temperature prediction with Mean Absolute Percentage Error (MAPE) values of 0.92 %, 0.72 %, and 1.3 % for average, maximum, and minimum temperatures, respectively. The defect detection process yields a detection accuracy (CR) of 96 % and sensitivity of detection (SD) of 89 %. This work is validated compared to the literature work done and by using K-fold cross validation technique. The proposed work emphasizes the improvement in defect detection accuracy and the overall quality enhancement of solar cells.
PubMed: 38828356
DOI: 10.1016/j.heliyon.2024.e31774 -
Journal of Global Antimicrobial... May 2024India's projected silica-dust-exposed workers will be 52 million at the end of 2025. Elimination of tuberculosis is also targeted in India by 2025. Scientists in India...
India's projected silica-dust-exposed workers will be 52 million at the end of 2025. Elimination of tuberculosis is also targeted in India by 2025. Scientists in India have already pointed out that unless silicosis is controlled, the said elimination is difficult to achieve. This study evidences an increasing incidence of tuberculosis and multidrug resistant tuberculosis (MDR-TB) with five deaths due to treatment failure among the silica dust-exposed workers compared to their unexposed counterparts. It was also observed that both tuberculosis as well as MDR-TB were directly proportional to the dose and/or duration of silica dust exposure. This means the incidence of MDR-TB is lowest in the unexposed group, moderate in the radiologically negative but silica dust exposed group (subradiological silicosis due to moderate exposure), and highest in the radiologically confirmed silicotic workers (maximally exposed group. Since India has a huge burden of silicosis, they are vulnerable to tuberculosis including multidrug-resistant tuberculosis resulting in the emergence of MDR-TB among the silica dust-exposed workers. This will also lead to a silent epidemic of silicotuberculosis in India shortly. Therefore, it would be important to have tools to quickly detect silicosis cases at an early stage to identify a vulnerable population and adopt an effective intervention measure.
PubMed: 38825150
DOI: 10.1016/j.jgar.2024.05.012 -
Environmental Research May 2024Bracken fern (Pteridium spp.) is a highly problematic plant worldwide due to its toxicity in combination with invasive properties on former farmland, in deforested areas... (Review)
Review
Bracken fern (Pteridium spp.) is a highly problematic plant worldwide due to its toxicity in combination with invasive properties on former farmland, in deforested areas and on disturbed natural habitats. The carcinogenic potential of bracken ferns has caused scientific and public concern for six decades. Its genotoxic effects are linked to illudane-type glycosides (ITGs), their aglycons and derivatives. Ptaquiloside is considered the dominating ITG, but with significant contributions from other ITGs. The present review aims to compile evidence regarding environmental pollution by bracken fern ITGs, in the context of their human and animal health implications. The ITG content in bracken fern exhibits substantial spatial, temporal, and chemotaxonomic variation. Consumption of bracken fern as food is linked to human gastric cancer but also causes urinary bladder cancers in bovines browsing on bracken. Genotoxic metabolites are found in milk and meat from bracken fed animals. ITG exposure may also take place via contaminated water with recent data pointing to concentrations at microgram/L-level following rain events. Airborne ITG-exposure from spores and dust has also been documented. ITGs may synergize with major biological and environmental carcinogens like papillomaviruses and Helicobacter pylori to induce cancer, revealing novel instances of chemical and biological co-carcinogenesis. Thus, the emerging landscape from six decades of bracken research points towards a global environmental problem with increasingly complex health implications.
PubMed: 38821456
DOI: 10.1016/j.envres.2024.119274