Search Results (770)

Objectives: This study assessed the effectiveness of a cognitive behavioral therapy (CBT)-based fine dust education program, grounded in the Health Belief Model (HBM), on elementary students’ fine dust knowledge, related behaviors, and mental health (depression, anxiety, stress, sleep quality). Methods: From September to November 2024, 95 students (grades 4–6) living near a coal-fired power plant in midwestern South Korea were assigned to either an intervention group (n = 44) or a control group (n = 51). The intervention group completed a three-session CBT-based education program; the control group received stress management education. Assessments were conducted at weeks 1, 2, 4, and 8 using standardized mental health and behavior scales (PHQ: Patient Health Questionnaire, GAD: Generalized Anxiety Disorder Assessment, PSS: Perceived Stress Scale, ISI: Insomnia Severity Index). Results: A chi-square test was conducted to compare pre- and post-test changes in knowledge and behavior related to PM2.5. The intervention group showed significant improvements in seven fine dust-related knowledge and behavior items (e.g., PM2.5 awareness rose from 33.3% to 75.0%; p < 0.05). The control group showed limited gains. Regarding mental health, based on a mixed-design ANCOVA, anxiety scores significantly declined over time in the intervention group, with group and interaction effects also significant (p < 0.05). Depression scores showed time effects, but group and interaction effects were not significant. No significant changes were observed for stress, sleep, or group × PM2.5 interactions. Conclusions: The CBT-based education program effectively enhanced fine dust knowledge, health behaviors, and reduced anxiety among students. It presents a promising, evidence-based strategy to promote environmental and mental health in school-aged children. Full article

Long-term source apportionment of PM_2.5 during high-pollution periods is essential for achieving sustained reductions in both PM_2.5 levels and their health impacts. This study conducted PM_2.5 sampling in Shenzhen from January to March over the years 2021–2024 to investigate the long-term impact of coronavirus disease 2019 and the short-term impact of the Spring Festival on PM_2.5 levels. The measured average PM_2.5 concentration during the research period was 22.5 μg/m³, with organic matter (OM) being the dominant component. Vehicle emissions, secondary sulfate, secondary nitrate, and secondary organic aerosol were identified by receptor model as the primary sources of PM_2.5 during the observational periods. The pandemic led to a decrease of between 30% and 50% in the contributions of most anthropogenic sources in 2022 compared to 2021, followed by a rebound. PM_2.5 levels in January–March 2024 dropped by 1.4 μg/m³ compared to 2021, mainly due to reduced vehicle emissions, secondary sulfate, fugitive dust, biomass burning, and industrial emissions, reflecting Shenzhen’s and nearby cities’ effective control measures. However, secondary nitrate and fireworks-related emissions rose significantly. During the Spring Festival, PM_2.5 concentrations were 23% lower than before the festival, but the contributions of fireworks burning exhibited a marked increase in both 2023 and 2024. Specifically, during intense peak events, fireworks burning triggered sharp, short-term spikes in characteristic metal concentrations, accounting for over 50% of PM_2.5 on those peak days. In the future, strict control over vehicle emissions and enhanced management of fireworks burning during special periods like the Spring Festival are necessary to reduce PM_2.5 concentration and improve air quality. Full article

Environmental comfort and air pollution are among the most important indicators for assessing the population’s quality of life in urban agglomerations. This study aims to explore long-term memory in air pollution time series by analyzing the dynamics of the Hurst exponent and evaluating the predictability index. This type of statistical pre-forecast analysis is essential for developing accurate forecasting models for such time series. The effectiveness of air quality monitoring systems largely depends on the precision of these forecasts. The Ekibastuz coal-mining center, which houses one of the largest coal-fired power stations in Kazakhstan and the world, with a capacity of about 4000 MW, was chosen as an example for the study. Data for the period from 1 March 2023 to 31 December 2024 were collected and analyzed at the Ekibastuz coal-fired power station. During the specified period, 14 indicators (67,527 observations) were collected at 10 min intervals, including mass concentrations of CO, NO, NO₂, SO₂, PM_2.5, and PM₁₀, as well as current mass consumption of CO, NO, NO₂, SO₂, dust, and NO_x. The detrended fluctuation analysis of a time series of air pollution indicators was used to calculate the Hurst exponent and identify long-term memory. Changes in the Hurst exponent in regards to dynamics were also investigated, and a predictability index was calculated to monitor emissions of pollutants in the air. Long-term memory is recorded in the structure of all the time series of air pollution indicators. Dynamic analysis of the Hurst exponent confirmed persistent time series characteristics, with an average Hurst exponent of about 0.7. Identifying the time series plots for which the Hurst exponent is falling (analysis of the indicator of dynamics), along with the predictability index, is a sign of an increase in the influence of random factors on the time series. This is a sign of changes in the dynamics of the pollutant release concentrations and may indicate possible excess emissions that need to be controlled. Calculating the dynamic changes in the Hurst exponent for the emission time series made it possible to identify two distinct clusters corresponding to periods of persistence and randomness in the operation of the coal-fired power station. The study shows that evaluating the predictability index helps fine-tune the parameters of time series forecasting models, which is crucial for developing reliable air pollution monitoring systems. The results obtained in this study allow us to conclude that the method of trended fluctuation analysis can be the basis for creating an indicator of the level of air pollution, which allows us to quickly respond to possible deviations from the established standards. Environmental services can use the results to build reliable monitoring systems for air pollution from coal combustion emissions, especially near populated areas. Full article

Accurately identifying the sources of fine particulate matter (PM_2.5) pollution is crucial for pollution control and public health protection. Taking the PM_2.5 pollution event that occurred in Suzhou in June 2023 as a typical case, this study analyzed the characteristics and components of PM_2.5, and quantified the contributions of meteorological conditions, regional transport, and local emissions to the summertime PM_2.5 surge in a typical Yangtze River Delta (YRD) city. Chemical composition analysis highlighted a sharp increase in nitrate ions (NO₃⁻, contributing up to 49% during peak pollution), with calcium ion (Ca²⁺) and sulfate ion (SO₄²⁻) concentrations rising to 2 times and 7.5 times those of clean periods, respectively. Results from the random forest model demonstrated that emission sources (74%) dominated this pollution episode, significantly surpassing the meteorological contribution (26%). The Weather Research and Forecasting model combined with the Community Multiscale Air Quality model (WRF–CMAQ) further revealed that local emissions contributed the most to PM_2.5 concentrations in Suzhou (46.3%), while external transport primarily originated from upwind cities such as Shanghai and Jiaxing. The findings indicate synergistic effects from dust sources, industrial emissions, and mobile sources. Validation using electricity consumption and key enterprise emission data confirmed that intensive local industrial activities exacerbated PM_2.5 accumulation. Recommendations include strengthening regulations on local industrial and mobile source emissions, and enhancing regional joint prevention and control mechanisms to mitigate cross-boundary transport impacts. Full article

This study investigates the emission characteristics and environmental impacts of pollutants from bagasse-fired biomass boilers through the integrated field monitoring of two sugarcane processing plants in Guangxi, China. Comprehensive analyses of flue gas components, including PM_2.5, NOx, CO, heavy metals, VOCs, HCl, and HF, revealed distinct physicochemical and emission profiles. Bagasse exhibited lower C, H, and S content but higher moisture (47~53%) and O (24~30%) levels compared to coal, reducing the calorific values (8.93~11.89 MJ/kg). Particulate matter removal efficiency exceeded 98% (water film dust collector) and 95% (bag filter), while NOx removal varied (10~56%) due to water solubility differences. Heavy metals (Cu, Cr, Ni, Pb) in fuel migrated to fly ash and flue gas, with Hg and Mn showing notable volatility. VOC speciation identified oxygenated compounds (OVOCs, 87%) as dominant in small boilers, while aromatics (60%) and alkenes (34%) prevailed in larger systems. Ozone formation potential (OFP: 3.34~4.39 mg/m³) and secondary organic aerosol formation potential (SOAFP: 0.33~1.9 mg/m³) highlighted aromatic hydrocarbons (e.g., benzene, xylene) as critical contributors to secondary pollution. Despite compliance with current emission standards (e.g., PM < 20 mg/m³), elevated CO (>1000 mg/m³) in large boilers indicated incomplete combustion. This work underscores the necessity of tailored control strategies for OVOCs, aromatics, and heavy metals, advocating for stricter fuel quality and clear emission standards to align biomass energy utilization with environmental sustainability goals. Full article

Large steam turbine generators are increasingly vulnerable to damage from shaft voltages and bearing currents due to the widespread adoption of modern power electronic excitation systems and more flexible operating regimes. Earthing brushes provide a critical path for discharging these shaft currents and voltages, but their effectiveness depends on the timely detection of brush degradation or faults. Conventional monitoring of shaft voltage and current is often rudimentary, typically limited to peak readings, making it challenging to identify specific fault conditions before mechanical damage occurs. This study addresses this gap by systematically analyzing shaft voltage and current signals under various controlled earthing brush fault conditions (floating brushes, worn brushes, and oil/dust contamination) in several large turbine generators. Experimental site tests identified distinct electrical signatures associated with each fault type, demonstrating that online shaft voltage and current measurements can reliably detect and classify earthing brush faults. These include unique RMS, DC, and harmonic patterns in both voltage and current signals, enabling accurate fault classification. These findings highlight the potential for more proactive maintenance and condition-based monitoring, which can reduce unplanned outages and improve the reliability and safety of power generation systems. Full article

Free-space optical (FSO) communication is a promising high-capacity solution for future wireless networks, particularly for backhaul and fronthaul links in 5G and emerging 6G systems. However, it remains highly vulnerable to environmental impairment, especially in arid regions prone to dust storms. While prior studies have addressed atmospheric effects such as fog and turbulence, the specific impact of dust on signal performance remains insufficiently explored. This work presents a probabilistic modeling framework for evaluating the performance of an intensity modulation/direct detection (IM/DD) FSO system under dust storm conditions. Using a controlled laboratory environment, we conducted measurements of the optical signal under dust-induced channel conditions using real-world dust samples collected from an actual dust storm. We identified the Beta distribution as the most accurate model for the measured signal fluctuations. Closed-form expressions were derived for average bit error rate (BER), outage probability, and channel capacity. The close agreement between the analytical, approximate, and simulated results validates the proposed model as a reliable tool for evaluating FSO system performance. The results show that the forward error correction (FEC) BER threshold of ${10}^{-3}$ is achieved at approximately 10.5 dB, and the outage probability drops below ${10}^{-3}$ at 10 dB average SNR. Full article

In this work, we evaluated the silvicultural and ecological parameters of Populus bolleana Lauche trees growing in conditions of anthropogenic pollution, using the example of one of the largest megacities of the Donetsk ridge, the city of Donetsk. The objectives of this study included determining the level of anthropogenic load of the territory; conducting dendrological studies to assess morphometric and allometric parameters, age structure, and condition of P. bolleana stands under the influence of environmental factors; as well as completing biomechanical studies to assess and predict the mechanical stability of stands. A total of 1109 plants growing in areas with increased anthropogenic load and in the control areas were studied. The model territories of the study were located in the city of Donetsk on Fallen Communards Avenue (length of field routes: 2.6 km) and Ilyicha Avenue (length of field routes: 9.7 km). Control plantings grew on the territory of the Donetsk botanical garden and residential (dormitory) districts of the city. The age structure of P. bolleana plantations remained uniform throughout the city for 50–55 years due to the fact that the landscaping was under a single state program. In the steppe zone in the south of the East European Plain, with a high level of anthropogenic load and severe natural climatic factors, the critical age of P. bolleana (55 years) was determined. The condition of plantations and their morphometric indices correlate with the level of anthropogenic load of the city (H, Dbase, DBH). Under control conditions, the plants are in good condition with signs of weakening (2 points). Under conditions of increased anthropogenic load, the plants are in a severely weakened condition (3 points). A total of 25% of the plants in the sample are in critical condition (4–5 points). The main damages to the crowns and trunks of plants include core rot, mechanical damage to bark and tissues, the development of core rot through the affected skeletal branch, crown thinning, and drying. P. bolleana trees are valued for their crown area and ability to retain dust particles from the air. The analysis of experimentally obtained data on the crown area showed that in the initial phases of ontogenesis, the average deviation in the crown area of plants does not depend on the place of growth. Due to artificial narrowing and sanitary pruning of the crown, as well as skeletal branches dying along the busiest highways, the values do not exceed 22–23 m² on average, with an allometric coefficient of 0.35–0.37. When comparing this coefficient in the control areas, the crown area in areas with a high level of anthropogenic load is 36 ± 11% lower. For trees growing under the conditions of the anthropogenic load of an industrial city and having reached the critical age, mechanical resistance varied depending on the study area and load level. At sites with a high level of pollution of the territory, a significant decrease in indicators was revealed in comparison with the control (mcr—71%, EI—75%, RRB—43%). Having analyzed all the obtained data, we can conclude that, until the age of 50–55 years, P. bolleana retains good viability, mechanical resistance, and general allometric ratios, upon which the stability of the whole plant depends. Even with modern approaches and tendencies toward landscaping with exotic introductions, it is necessary to keep P. bolleana as the main species in dendrobanocenoses. Full article

Identifying the coupling effect mechanisms of particulate matter (PM) in different functional areas on the atmospheric environment will help to carry out graded precision prevention and control measures against pollution within mining cities. This study monitored the pollution of three different functional areas in Wuhai, a typical mining city in Inner Mongolia. PM₁, PM_2.5, PM₁₀, and TSP were sampled and analyzed for chemical fractions both in the daytime and at night in spring, summer, autumn, and winter. The results showed that the average daily concentrations of PM were generally higher in the mining area than in the urban and sandy areas in different seasons. The results of the Kerriging analysis showed that the urban area was affected the most when specific ranges of high PM concentrations were detected in the mining area and specific ranges of low PM concentrations were detected in the sandy area. PMF results indicated that the source of pollutants in different functional areas and seasons were dust, industrial and traffic emissions, combustion, and sea salt. The contributions of dust in PM with different particle sizes in the mining and sandy areas were as high as 49–72%, while all the pollutant sources accounted for a large proportion of pollution in the urban area. In addition, dust was the largest source of pollution in summer and winter, and the contribution of combustion sources to pollution was higher in winter. Health risks associated with Cr were higher in the sandy area, and non-carcinogenic risks associated with Mn were higher in the mining area during spring and summer, while there was a greater impact on human health in the urban area during autumn and winter. The results of this study revealed the coupling effect mechanisms of different functional areas on the local atmospheric environment and contribute to the development of regional atmospheric defense and control policies. Full article

This study examines the influence mechanism of mining depth evolution on dust distribution, using the An Tai Bao open-pit coal mine as the research subject. A spatial coordinate system of the mining area was established utilizing a GIS positioning system, and high-resolution topographic data were extracted using Global Mapper. The research team developed a three-dimensional geological model updating algorithm with depth gradient as the characteristic parameter, enabling dynamic monitoring of mining depth with a model iteration accuracy of 0.5 m per update. A Fluent-based numerical simulation method was employed to construct a depth-dependent dust migration field solving system, aiming to elucidate the three-dimensional coupling mechanism between mining depth and dust dispersion. The findings reveal that mining depth demonstrates a three-stage critical response to dust migration. When the depth surpasses the threshold of 150 m, the wind speed attenuation rate at the pit bottom exhibits a marked change, and the dust dispersion distance decreases by 62% compared to shallow mining conditions. The slope pressure field evolution shows a significant depth-enhancement effect, with the maximum wind pressure at the leeward step boundary increasing by 22–35% for every additional 50 m of depth, resulting in dust accumulation zones with distinct depth-related characteristics. The west wind scenario demonstrates a particularly notable depth amplification effect, with the dust dispersion range in a 200-meter-deep pit expanding by 53.7% compared to the standard west wind condition. Furthermore, the interaction between particle size and depth causes the dust migration distance to exhibit exponential decay as depth increases. This research elucidates the progressive constraining influence of mining depth, a critical control parameter, on dust migration patterns. It establishes a depth-oriented theoretical framework for dust prevention and control strategies in deep open-pit mines. Full article

Aerosols considerably reduce the upwelling radiance in the thermal infrared (IR) window; thus, it is worthwhile to understand the effects and challenges of assimilating aerosol-affected (i.e., hazy-sky) IR observations for all-sky data assimilation (DA). This study introduces an aerosol-aware DA framework for the Infrared Atmospheric Sounder Interferometer (IASI) to exploit hazy-sky IR observations and investigate the impact of assimilating hazy-sky IR observations on analyses and subsequent forecasts. The DA framework consists of the detection of hazy-sky pixels and an observation error model as the function of the aerosol effect. Compared to the baseline experiment, the experiment utilized an aerosol-aware framework that reduces biases in the sea surface temperature in the tropical region, particularly over the areas affected by heavy dust plumes. There are no significant differences in the evaluation of the analyses and the 7-day forecasts between the experiments. To further improve the aerosol-aware framework, the enhancements in quality control (e.g., aerosol detection) and bias correction need to be addressed in the future. Full article

This study compares long-term air pollution trends and seasonal patterns in Beijing and Seoul from 2014 to 2024, focusing on PM2.5, PM10, CO, NO₂, SO₂, and O₃. Using statistical analyses including Mann–Kendall tests and generalized additive models, we found that Beijing achieved notable reductions in particulate matter, largely due to stricter industrial controls and reduced coal use, though winter pollution peaks remain. In contrast, Seoul’s improvements were slower, mainly due to persistent vehicular emissions and recurring spring dust storms from northern China. Seasonal analysis showed winter peaks in Beijing linked to coal heating, and spring peaks in Seoul driven by transboundary dust, with higher summer ozone in Seoul reflecting photochemical activity. These findings highlight the need for city-specific air quality management and regional cooperation, recommending further reductions in vehicular emissions for Seoul and continued transition from coal in Beijing to mitigate health impacts. This study identifies specific seasonal trends and pollution sources that require targeted policy interventions to improve air quality. Full article

Niobium, with its unique properties, plays a key role in high-tech industries, but its recovery from secondary sources remains poorly studied. The kinetics of niobium leaching from niobium-containing middlings obtained via the water treatment of dust chamber sublimations of titanium chlorinators is considered in this study. The leaching process was conducted using a fluoride–sulphuric acid solution. The experiments were performed at 25–90 °C in agitation mode. Kinetic data were analysed using compression-core and mixed-control models, which made it possible to establish the limiting stages of the process. A mixed mechanism, including a chemical reaction on the surface and diffusion through a layer of products with an activation energy of 30.05 kJ/mol, was established. The niobium recovery degree increased from 35.25 to 93.5% as the temperature increased, highlighting its effect on the process. The insoluble residue, rich in titanium, and the liquid phase with niobium and zirconium have the potential for further processing. The results provide the basis to optimise technologies intended to recover niobium from man-made raw materials, contributing to an increase in resource efficiency. Full article

Background: Lithium is an essential commodity; however, its mining and processing can expose miners to airborne contaminants such as inhalable dust, respirable dust and respirable crystalline silica. These exposures may adversely affect respiratory health. To protect the health of miners, exposure assessment and control activities are required, followed by respiratory health monitoring to assess the effect of exposure on respiratory health. This study aimed to investigate the relationship between workgroup exposure to airborne contaminants and respiratory health. To determine group exposure levels, exposure data was collected at the group level, which limits individual-level inference, followed by health monitoring. Methods: Industry health monitoring data were collected from miners in three surface lithium mines in Western Australia for the period between October 2023 and October 2024. Miners from Management Administration & Technical, Crusher/Dry/Wet Plant, and Laboratory Operations participated in a pulmonary function test, completed a health and exposure questionnaire and underwent a low dose high-resolution computed tomography. Multivariable linear and logistic regression models were fitted to identify factors associated with lung function and respiratory symptoms. Results: Older age, smoking and pre-existing respiratory conditions were correlated with poor respiratory airflow. The odds of having a respiratory obstruction or restriction were significantly higher by 3.942 and 2.165 times respectively, for miners who were 40 years old or above, and those who had existing diagnosed respiratory medical conditions. The risk of coughing among current smokers was more than four times higher compared to non-smokers. In addition, working in Crushing and Processing was significantly associated with the risk of experiencing respiratory symptoms compared to working in Management Administration & Technical and Laboratory Operations. Conclusions: The study demonstrated that respiratory health was influenced by non-work-related risk factors. Based on these results, it is recommended that health promotion programs be developed and implemented to empower miners to cease smoking and to manage non-work-related respiratory conditions. Full article

Background/Objectives: Asthma, a chronic airway inflammatory disease characterized by bronchial narrowing and caused by an inflammatory response, results in airway obstruction and hyperresponsiveness. Stachydrine (STA), an abundant metabolite found in plants and humans, is recognized for its bioactivity in treating fibrosis, cancer, and inflammation. However, its effects on asthma have not been fully elucidated. We aimed to investigate the ameliorating effects of STA on chronic airway inflammation caused by Dermatophagoides pteronyssinus (house dust mite, HDM). Methods: We used a murine model of HDM-induced airway inflammation to assess the change in metabolite profile by chronic airway inflammation. The mice were challenged with HDM (35 challenges in total) for up to 12 weeks. Serum metabolites were analyzed using capillary electrophoresis time-of-flight mass spectrometry. Results: HDM exposure increased airway hypersensitivity, immune cell infiltration, cytokine production, goblet cell hyperplasia, collagen deposition, and alpha smooth muscle actin and fibronectin expression. Serum metabolite analysis revealed that STA levels were lower in the mice with HDM-induced chronic inflammation than in the controls. In vitro analyses demonstrated that HDM sensitization increased cytokine production (interleukin [IL]-6 and IL-8) and extracellular signal-regulated kinase (ERK) activity. However, STA treatment reduced HDM-induced IL-6 and IL-8 production and ERK activity. Co-treatment with a mitogen-activated protein kinase (MAPK) inhibitor and STA resulted in a more pronounced reduction in cytokine production and MAPK activity. Conclusions: These findings suggest that STA, particularly when used in combination with a MAPK inhibitor, effectively suppresses airway inflammation through ERK pathway inhibition, making it a potential therapeutic agent for asthma treatment. Full article