Search Results (1,329)

Considering the serious hazard of respiratory dust in underground coal mines and the low efficiency of traditional dust-reduction technology, this study optimizes the atomisation parameters of the gas–liquid two-phase flow nozzle through numerical simulation and experimental testing, and designs an on-board dust-reduction system. Based on the Fluent software (version 2023 R2), a flow field model outside the nozzle was established, and the effects of the air supply pressure, gas-phase inlet velocity, and droplet mass flow rate on the atomisation characteristics were analyzed. The results show that increasing the air supply pressure can effectively reduce the droplet particle size and increase the range and atomisation angle, and that the dust-reduction efficiency is significantly improved with the increase in pressure. The dust-reduction efficiency reached 69.3% at 0.6 MPa, which was the economically optimal operating condition. Based on the parameter optimization, this study designed an annular airborne gas–liquid two-phase flow dust-reduction system, and a field test showed that the dust-reduction efficiency of this system could reach up to 86.0%, which is 53.5% higher than that of traditional high-pressure spraying, and that the dust concentration was reduced to less than 6 mg/m³. This study provides an efficient and reliable technical solution for the management of underground coal mine dust and guidance for promoting the development of the coal industry. 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

The occurrence of haze pollution significantly deteriorates air quality and threatens human health, yet persistent knowledge gaps in real-time source apportionment of fine particulate matter (PM_2.5) hinder sustained improvements in atmospheric pollution conditions. Thus, this study employed single-particle aerosol mass spectrometry (SPAMS) to investigate PM_2.5 sources and dynamics during winter haze episodes in Yinchuan, Northwest China. Results showed that the average PM_2.5 concentration was 57 μg·m⁻³, peaking at 218 μg·m⁻³. PM_2.5 was dominated by organic carbon (OC, 17.3%), mixed carbonaceous particles (ECOC, 17.0%), and elemental carbon (EC, 14.3%). The primary sources were coal combustion (26.4%), fugitive dust (25.8%), and vehicle emissions (19.1%). Residential coal burning dominated coal emissions (80.9%), highlighting inefficient decentralized heating. Source contributions showed distinct diurnal patterns: coal combustion peaked nocturnally (29.3% at 09:00) due to heating and inversions, fugitive dust rose at night (28.6% at 19:00) from construction and low winds, and vehicle emissions aligned with traffic (17.5% at 07:00). Haze episodes were driven by synergistic increases in local coal (+4.0%), dust (+2.7%), and vehicle (+2.1%) emissions, compounded by regional transport (10.1–36.7%) of aged particles from northwestern zones. Fugitive dust correlated with sulfur dioxide (SO₂) and ozone (O₃) (p < 0.01), suggesting roles as carriers and reactive interfaces. Findings confirm local emission dominance with spatiotemporal heterogeneity and regional transport influence. SPAMS effectively resolved short-term pollution dynamics, providing critical insights for targeted air quality management in arid regions. Full article

Clean and safe air quality is essential for public health, yet particulate matter (PM) significantly degrades air quality and poses serious health risks. The Gulf Cooperation Council (GCC) countries are particularly vulnerable to frequent and intense dust storms due to their vast desert landscapes. This study presents the first health risk assessment of carcinogenic and non-carcinogenic risks associated with exposure to PM2.5 and PM10 bound heavy metals and polycyclic aromatic hydrocarbons (PAHs) based on air quality data collected during the years of 2016–2018 near Dubai International Airport and Abu Dhabi International Airport. The results reveal no significant carcinogenic risks for lead (Pb), cobalt (Co), nickel (Ni), and chromium (Cr). Additionally, AI-based regression analysis was applied to time-series dust monitoring data to enhance predictive capabilities in environmental monitoring systems. The estimated incremental lifetime cancer risk (ILCR) from PAH exposure exceeded the acceptable threshold (10⁻⁶) in several samples at both locations. The relationship between visibility and key environmental variables—PM1, PM2.5, PM10, total suspended particles (TSPs), wind speed, air pressure, and air temperature—was modeled using three machine learning algorithms: linear regression, support vector machine (SVM) with a radial basis function (RBF) kernel, and artificial neural networks (ANNs). Among these, SVM with an RBF kernel showed the highest accuracy in predicting visibility, effectively integrating meteorological data and particulate matter variables. These findings highlight the potential of machine learning models for environmental monitoring and the need for continued assessments of air quality and its health implications in the region. Full article

Air quality forecasts play a crucial role in informing the public about atmospheric pollutant levels that pose risks to human health and the environment. The accuracy of these forecasts strongly depends on the quality and resolution of the input data used in the modelling process. At HungaroMet, the Hungarian Meteorological Service, the CHIMERE chemical transport model is used to provide two-day air quality forecasts for the territory of Hungary. This study compares two configurations of the CHIMERE model: the current operational setup, which uses climatological averages from the LMDz-INCA database for boundary conditions, and a test configuration that incorporates real-time boundary conditions from the CAMS global forecast. The primary objective of this work was to assess how the use of real-time versus climatological boundary conditions affects modelled concentrations of key pollutants, including NO₂, O₃, PM₁₀, and PM_2.5. The model results were evaluated against observational data from the Hungarian Air Quality Monitoring Network using a range of statistical metrics. The results indicate that the use of real-time boundary conditions, particularly for aerosol-type pollutants, improves the accuracy of PM₁₀ forecasts. This improvement is most significant under meteorological conditions that favour the long-range transport of particulate matter, such as during Saharan dust or wildfire episodes. These findings highlight the importance of incorporating dynamic, up-to-date boundary data, especially for particulate matter forecasting—given the increasing frequency of transboundary dust events. 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

Accurate short-term forecasting of photovoltaic power generation is vital for the operational stability of isolated energy systems, especially in regions with increasing renewable energy penetration. This study presents a novel AI-based forecasting framework applied to the island of Cyprus. Using machine learning methods, particularly regression trees, the proposed approach evaluates the impact of key environmental variables on PV performance, with an emphasis on atmospheric dust transport and air composition variability. A distinguishing feature of this work is the integration of cross-continental dust events and diverse atmospheric parameters into a structured forecasting model. A new clustering methodology is introduced to classify these inputs and analyze their correlation with PV output, enabling improved feature selection for model training. Importantly, all input parameters are sourced from publicly accessible, internet-based platforms, facilitating wide reproducibility and operational application. The obtained results demonstrate that incorporating dust deposition and air composition features significantly enhances forecasting accuracy, particularly during severe dust episodes. This research not only fills a notable gap in the PV forecasting literature but also provides a scalable model for other dust-prone regions transitioning to high levels of solar energy integration. Full article

The cement industry, a foundation of global infrastructure development, significantly contributes to environmental pollution. Key sources of pollution include dust emissions; greenhouse gases, particularly carbon dioxide; and the release of toxic substances such as heavy metals and particulate matter. These pollutants contribute to air, water, and soil degradation and are linked to severe health conditions in nearby populations, including respiratory disorders, cardiovascular diseases, and increased mortality rates. Noise pollution is also a significant issue, inducing auditory diseases that affect most workers in cement plants, and disturbing the population living in the neighborhoods and fauna behavior. This review explores the pollution paths and the multifaceted impacts of cement production on the environment. It also highlights the social challenges faced by communities, underscoring the urgent need for stricter environmental policies and the adoption of greener technologies to mitigate the adverse effects of cement production on both the environment and human health. Full article

Ponderosa pine (Pinus ponderosa Lawson & C. Lawson) forests in the western United States have experienced reduced fire frequency since Euro-American settlement, usually because of successful fire suppression policies and even without such human impacts at remote sites in the Great Basin and Mojave Deserts. In an effort to improve our understanding of long-term environmental dynamics in sky-island ecosystems, we developed tree-ring chronologies from ponderosa pines located in the Sheep Mountain Range of southern Nevada, inside the Desert National Wildlife Refuge (DNWR). After comparing those dendrochronological records with other ones available for the south-central Great Basin, we analyzed their climatic response using station-recorded monthly precipitation and air temperature data from 1950 to 2024. The main climatic signal was December through May total precipitation, which was then reconstructed at annual resolution over the past five centuries, from 1490 to 2011 CE. The mean episode duration was 2.6 years, and the maximum drought duration was 11 years (1924–1934; the “Dust Bowl” period), while the longest episode, 19 years (1905–1923), is known throughout North America as the “early 1900s pluvial”. By quantifying multi-annual dry and wet episodes, the period since DNWR establishment was placed in a long-term dendroclimatic framework, allowing us to estimate the potential drought resilience of its unique, tree-dominated environments. Full article

Dust emissions from unpaved haul roads in open-pit coal mining pose a significant risk to air quality, health, and operational efficiency of mining operations. This study integrated real-time field monitoring with numerical simulations using ANSYS Fluent 2023 R1 to investigate the generation, dispersion, and migration of particulate matter (PM) at the Ha’erwusu open-pit coal mine under varying meteorological conditions. Real-time measurements of PM2.5, PM10, and TSP, along with meteorological variables (wind speed, wind direction, humidity, temperature, and air pressure), were collected and analyzed using Pearson’s correlation and multivariate linear regression analyses. Wind speed and air pressure emerged as dominant factors in winter, whereas wind and temperature were more influential in summer (R² = 0.391 for temperature vs. PM2.5). External airflow simulations revealed that truck-induced turbulence and high wind speeds generated wake vortices with turbulent kinetic energy (TKE) peaking at 5.02 m²/s², thereby accelerating particle dispersion. The dust migration rates reached 3.33 m/s within 6 s after emission and gradually decreased with distance. The particle settling velocities ranged from 0.218 m/s for coarse dust to 0.035 m/s for PM2.5, with dispersion extending up to 37 m downwind. The highest simulated dust concentration reached 4.34 × 10⁻² g/m³ near a single truck and increased to 2.51 × 10⁻¹ g/m³ under multiple-truck operations. Based on spatial attenuation trends, a minimum safety buffer of 55 m downwind and 45 m crosswind is recommended to minimize occupational exposure. These findings contribute to data-driven, weather-responsive dust suppression planning in open-pit mining operations and establish a validated modeling framework for future mitigation strategies in this field. 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

Allergic diseases, particularly respiratory allergies like asthma and allergic rhinitis, are a growing public health concern influenced by environmental factors such as climate change and air pollution. The exposome framework enables a comprehensive assessment of how lifelong environmental exposures shape immune responses and allergic sensitization. Peru’s diverse ecosystems and climates provide a unique setting to investigate regional variations in allergic sensitization. This study characterized these patterns in five Peruvian regions with distinct climatic, urbanization, and socioeconomic characteristics. A total of 268 individuals from Lima, Piura, Tarapoto, Arequipa, and Tacna were analysed for allergen-specific IgE responses using a multiplex IgE detection system. The results revealed significant geographical differences in sensitization frequencies and serodominance profiles, based on descriptive statistics and supported by Chi-square comparative analysis. House dust mites were predominant in humid regions, while Arequipa exhibited higher sensitization to cat allergens. In Tacna, olive pollen showed notable prevalence alongside house dust mites. Tarapoto’s high humidity correlated with increased fungal and cockroach allergen sensitization. Notably, some allergens traditionally considered minor, such as Der p 5 and Der p 21, reached sensitization prevalences close to or exceeding 50% in certain regions. These findings provide the most detailed molecular characterization of allergic sensitization in Peru to date, highlighting the importance of region-specific allergy management strategies. Understanding environmental influences on allergic diseases can support more effective diagnostic, therapeutic, and preventive approaches tailored to diverse geographical contexts. Full article

Dust aerosols significantly affect climate and air quality in Northwest China (30–50° N, 70–110° E), where frequent dust storms complicate accurate aerosol classification when using CALIPSO satellite data. This study introduces an Enhanced 1D U-Net model to enhance dust aerosol retrieval, incorporating Inception modules for multi-scale feature extraction, Transformer blocks for global contextual modeling, CBAM attention mechanisms for improved feature selection, and residual connections for training stability. Using CALIPSO Level 1B and Level 2 Vertical Feature Mask (VFM) data from 2015 to 2020, the model processed backscatter coefficients, polarization characteristics, and color ratios at 532 nm and 1064 nm to classify aerosol types. The model achieved a precision of 94.11%, recall of 99.88%, and F1 score of 96.91% for dust aerosols, outperforming baseline models. Dust aerosols were predominantly detected between 0.44 and 4 km, consistent with observations from CALIPSO. These results highlight the model’s potential to improve climate modeling and air quality monitoring, providing a scalable framework for future atmospheric research. Full article

In this study, a large portion of data on the chemical composition of precipitation falling in the South Baikal region shows the main factors determining their formation in 2017–2024. Taking into account the high variability of meteorological conditions in the region, both in time and in space, a method of observing the chemical composition of atmospheric precipitation has been developed, which makes it possible to determine its composition depending on the conditions of air mass formation. Using statistical analysis, marker substances characterizing the main groups of sources influencing the composition of atmospheric precipitation were identified. Joint analysis of air mass trajectories and data on chemical composition of precipitation allowed for establishing the areas of location of potential sources of precipitation pollution. All precipitation events were categorized based on the similarity of air mass formation conditions and chemical composition. Precipitation composition data collected on the shores of Lake Baikal reflect the influence of different types of pollutants such as industrial emissions, motor vehicles, dust storms, and forest fires. The results of the study are relevant for air quality assessment in the region and demonstrate the potential of using precipitation chemistry data to understand the long-range transport of pollutants, which contributes to sustainable development by increasing the availability of air quality data in ecologically significant regions such as Lake Baikal. Full article