Search Results (212)

Rapid industrialization and urbanization have progressed in Korea, yet public attention to hazardous pollutants emitted from industrial complexes remains limited. With the increasing coexistence of industrial and residential areas, there is a growing need for real-time monitoring and management plans that account for the rapid dispersion of hazardous air pollutants (HAPs). In this study, we conducted spatiotemporal data collection and analysis for the first time in Korea using real-time measurements obtained through mobile extractive differential optical absorption spectroscopy (Me-DOAS) mounted on a solar occultation flux (SOF) vehicle. The measurements were conducted in the Saha Sinpyeong–Janglim Industrial Complex in Busan, which comprises the Sasang Industrial Complex and the Sinpyeong–Janglim Industrial Complex. BTEX compounds were selected as target volatile organic compounds (VOCs), and real-time measurements of both BTEX and fine particulate matter (PM) were conducted simultaneously. Correlation analysis revealed a strong relationship between PM₁₀ and PM_2.5 (r = 0.848–0.894), indicating shared sources. In Sasang, BTEX levels were associated with traffic and localized facilities, while in Saha Sinpyeong–Janglim, the concentrations were more influenced by industrial zoning and wind patterns. Notably, inter-compound correlations such as benzene–m-xylene and p-xylene–toluene suggested possible co-emission sources. This study proposes a GIS-based, three-dimensional air quality management approach that integrates variables such as traffic volume, wind direction, and speed through real-time measurements. The findings are expected to inform effective pollution control strategies and future environmental management plans for industrial complexes. Full article

The Latin American and Caribbean (LAC) region faces persistent challenges of inequality, climate change vulnerability, and deteriorating air quality. The Aburrá Valley, where Medellín is located, is a narrow tropical valley with complex topography, strong thermal inversions, and unstable atmospheric conditions, all of which exacerbate the accumulation of pollutants. In Medellín, ${\mathrm{NO}}_2$ concentrations have remained nearly unchanged over the past eight years, consistently approaching critical thresholds, despite the implementation of air quality control strategies. These persistent high concentrations are closely linked to the variability of the atmospheric boundary layer (ABL) and are often intensified by prolonged dry periods. This study focuses on a representative street canyon in Medellín that has undergone recent urban interventions, including the construction of new public spaces and pedestrian areas, without explicitly considering their impact on ${\mathrm{NO}}_x$ dispersion. Using Computational Fluid Dynamics (CFD) simulations, this work evaluates the influence of urban morphology on ${\mathrm{NO}}_x$ accumulation. The results reveal that areas with high Aspect Ratios (AR > 0.65) and dense vegetation exhibit reduced wind speeds at the pedestrian level—up to 40% lower compared to open zones—and higher ${\mathrm{NO}}_2$ concentrations, with maximum simulated values exceeding 50 μg/m³. This study demonstrates that the design of pedestrian corridors in complex urban environments like Medellín can unintentionally create pollutant accumulation zones, underscoring the importance of integrating air quality considerations into urban planning. The findings provide actionable insights for policymakers, emphasizing the need for comprehensive modeling and field validation to ensure healthier urban spaces in cities affected by persistent air quality issues. Full article

Ammonia (NH₃) emissions affect the environment, climate and human health and originate mainly from agricultural sources like synthetic nitrogen fertilizers. Accurate and replicable measurements of NH₃ emissions are crucial for research, inventories and evaluation of mitigation measures. There exist specific application limitations of NH₃ emission measurement techniques and a high variability in method performance between studies, in particular from small plots. Therefore, the aim of this study was the assessment of measurement methods for ammonia emissions from replicated small plots. Methods were evaluated in 18 trials on six sites in Germany (2021–2022). Urea was applied to winter wheat as an emission source. Two small-plot methods were employed: inverse dispersion modelling (IDM) with atmospheric concentrations obtained from Alpha samplers and the dynamic chamber Dräger tube method (DTM). Cumulative NH₃ losses assessed by each method were compared to the results of the integrated horizontal flux (IHF) method using Alpha samplers (Alpha IHF) as a micrometeorological reference method applied in parallel large-plot trials. For validation, Alpha IHF was also compared to IHF/ZINST with Leuning passive samplers. Cumulative NH₃ emissions assessed using Alpha IHF and DTM showed good agreement, with a relative root mean square error (rRMSE) of 11%. Cumulative emissions assessed by Leuning IHF/ZINST deviated from Alpha IHF, with an rRMSE of 21%. For low-wind-speed and high-temperature conditions, NH₃ losses detected with Alpha IDM had to be corrected to give acceptable agreement (rRMSE 20%, MBE +2 kg N ha⁻¹). The study shows that quantification of NH₃ emissions from small plots is feasible. Since DTM is constrained to specific conditions, we recommend Alpha IDM, but the approach needs further development. Full article

Mine ventilation systems are critical for ensuring operational safety, yet air leakage remains a pervasive challenge, leading to energy inefficiency and heightened safety risks. Traditional tracer gas methods, while effective in simple networks, exhibit significant errors in complex multi-entry systems due to static empirical parameters and environmental interference. This study proposes an integrated methodology that combines multi-path airflow analysis with dynamic longitudinal dispersion coefficient correction to enhance the accuracy of air leakage detection. Utilizing sulfur hexafluoride (SF6) as the tracer gas, a phased release protocol with temporal isolation was implemented across five strategic points in a coal mine ventilation network. High-precision detectors (Bruel & Kiaer 1302) and the MIVENA system enabled synchronized data acquisition and 3D network modeling. Theoretical models were dynamically calibrated using field-measured airflow velocities and dispersion coefficients. The results revealed three deviation patterns between simulated and measured tracer peaks: Class A deviation showed 98.5% alignment in single-path scenarios, Class B deviation highlighted localized velocity anomalies from Venturi effects, and Class C deviation identified recirculation vortices due to abrupt cross-sectional changes. Simulation accuracy improved from 70% to over 95% after introducing wind speed and dispersion adjustment coefficients, resolving concealed leakage pathways between critical nodes and key nodes. The study demonstrates that the dynamic correction of dispersion coefficients and multi-path decomposition effectively mitigates errors caused by turbulence and geometric irregularities. This approach provides a robust framework for optimizing ventilation systems, reducing invalid airflow losses, and advancing intelligent ventilation management through real-time monitoring integration. 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

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

The inherent intermittency and uncertainty of renewable energy generation pose significant challenges to the safe and stable operation of power grids, particularly when power demand does not match renewable energy supply, leading to issues such as wind and solar power curtailment. To effectively promote the consumption of renewable energy while leveraging electric vehicles (EVs) in virtual power plants (VPPs) as distributed energy storage resources, this paper proposes an ordered scheduling strategy for EVs in campus areas oriented towards renewable energy consumption. Firstly, to address the uncertainty of renewable energy output, this paper uses Conditional Generative Adversarial Network (CGAN) technology to generate a series of typical scenarios. Subsequently, a mathematical model for EV aggregation is established, treating the numerous dispersed EVs within the campus as a collectively controllable resource, laying the foundation for their ordered scheduling. Then, to maximize renewable energy consumption and optimize EV charging scheduling, an improved Particle Swarm Optimization (PSO) algorithm is adopted to solve the problem. Finally, case studies using a real-world testing system demonstrate the feasibility and effectiveness of the proposed method. By introducing a dynamic inertia weight adjustment mechanism and a multi-population cooperative search strategy, the algorithm’s convergence speed and global search capability in solving high-dimensional non-convex optimization problems are significantly improved. Compared with conventional algorithms, the computational efficiency can be increased by up to 54.7%, and economic benefits can be enhanced by 8.6%. Full article

PM_2.5 pollution poses significant risks to human health and the environment, underscoring the importance of accurate PM_2.5 simulation. This study simulated a representative PM_2.5 pollution event using the Weather Research and Forecasting model coupled with chemistry (WRF-Chem), incorporating the assimilation of infrared atmospheric motion vector (AMV) data from the Fengyun-4A (FY-4A) satellite. A comprehensive analysis was conducted to examine the meteorological characteristics of the event and their influence on PM_2.5 concentration simulations. The results demonstrate that the assimilation of FY-4A infrared AMV data significantly enhanced the simulation performance of meteorological variables, particularly improving the wind field and capturing local and small-scale wind variations. Moreover, PM_2.5 concentrations simulated with AMV assimilation showed improved spatial and temporal agreement with ground-based observations, reducing the root mean square error (RMSE) by 8.2% and the mean bias (MB) by 15.2 µg/m³ relative to the control (CTL) experiment. In addition to regional improvements, the assimilation notably enhanced PM_2.5 simulation accuracy in severely polluted cities, such as Tangshan and Tianjin. Mechanistic analysis revealed that low wind speeds and weak atmospheric divergence restricted pollutant dispersion, resulting in higher near-surface concentrations. This was exacerbated by cooler nighttime temperatures and a lower planetary boundary layer height (PBLH). These findings underscore the utility of assimilating satellite-derived wind products to enhance regional air quality modeling and forecasting accuracy. This study highlights the potential of FY-4A infrared AMV data in improving regional pollution simulations, offering scientific support for the application of next-generation Chinese geostationary satellite data in numerical air quality forecasting. Full article

To achieve the precise quantification and real-time monitoring of CO₂ emissions from stationary sources, this study developed a Gaussian plume model-based dispersion framework incorporating emission characteristics. Critical factors affecting CO₂ dispersion were systematically analyzed, with model optimization conducted through plume rise height adjustments and reflection coefficient calibrations. MATLAB-based simulations on an industrial park case study demonstrated that wind speed, atmospheric stability, and effective release height constituted pivotal determinants for enhancing CO₂ dispersion modeling accuracy. Furthermore, the inverse estimation of source strength at emission terminals was implemented via particle swarm optimization, establishing both theoretical foundations and methodological frameworks for the precision monitoring and predictive dispersion analysis of stationary-source CO₂ emissions. Full article

Effectively managing carbon monoxide (CO) pollution in complex industrial cities like Jubail remains challenging due to the diversity of emission sources and local environmental dynamics. This study analyzes spatiotemporal CO patterns and builds accurate predictive models using five years (2018–2022) of data from ten monitoring stations, combined with meteorological variables. Exploratory analysis revealed distinct diurnal and moderate weekly CO cycles, with prevailing northwesterly winds shaping dispersion. Spatial correlation of CO was low (average 0.14), suggesting strong local sources, unlike temperature (0.92) and wind (0.5–0.6), which showed higher spatial coherence. Seasonal Trend decomposition (STL) confirmed stronger seasonality in meteorological factors than in CO levels. Low wind speeds were associated with elevated CO concentrations. Key predictive features, such as 3-h rolling mean and median values of CO, dominated feature importance. Spatiotemporal analysis highlighted persistent hotspots in industrial areas and unexpectedly high levels in some residential zones. A range of models was tested, with ensemble methods (Extreme Gradient Boosting (XGBoost) and Categorical Boosting (CatBoost)) achieving the best performance ($R^2>0.95$) and XGBoost producing the lowest Root Mean Squared Error (RMSE) of 0.0371 ppm. This work enhances understanding of CO dynamics in complex urban–industrial areas, providing accurate predictive models ($R^2>0.95$) and highlighting the importance of local sources and temporal patterns for improving air quality forecasts. Full article

With the extensive use of liquefied petroleum gas (LPG) in the catering industry, leakage explosions have become frequent. This study employs numerical simulations to investigate the diffusion patterns of LPG leakage under various ventilation conditions. The results show that there is a logarithmic relationship between the wind speed and the volume of a propane gas cloud under natural ventilation. In the wind speed ranges of 1.5 to 3.3 m/s and 7.9 to 10.7 m/s, a small increase in wind speed leads to a significant reduction in gas cloud volume (97.2% and 95.05%, respectively). Under forced ventilation, the volume of the gas cloud decreases by 90.6%, from 6.67 m³ at higher air exchange rates (22.1 and 24.3 times/h), reducing explosion risks. When leakage occurs at the stove, the optimal combination for dispersing the propane combustible gas cloud is window opening at position 1 and fan at position a. The volume of the gas cloud at window position 1 increases exponentially with the distance between the fan and the leak source. The fan is installed within 2.786 m from the leak source to ensure that the gas cloud volume remains below 0.5 m³. These findings provide valuable insights for the design and the optimization of ventilation systems and layouts in commercial kitchens. Full article

Optimizing the wind environment within university dormitory areas is essential for ensuring student safety, enhancing living comfort, and improving building energy efficiency. In this study, the wind environment of multi-story university dormitories in cold regions is comprehensively investigated through computational fluid dynamics (CFD) simulations conducted with the PHONECIS software (version 2019), combined with orthogonal experimental design methods for systematic analysis and optimization. Through orthogonal experimental design, the effects of key morphological parameters—including building layout, length, width, and height—on the near-ground wind environment were evaluated. Among these, building width exerted the greatest influence, followed by building length, layout form, and finally building height. Based on the analysis, the optimal design scheme features a staggered building layout, with individual dormitory buildings measuring 60 m in length, 16 m in width, and 11.4 m in height. This optimized design was implemented in the multi-story dormitory area of the eastern section of Chang’an University’s New Campus. A comparative analysis of wind speed distribution before and after optimization, conducted specifically for the outdoor spaces during the winter season, revealed that the average near-ground wind speed was reduced from 3.3 m/s to 2.7 m/s, achieving an 18% reduction. The staggered arrangement and adjusted building proportions effectively dispersed airflow, mitigated high-velocity zones, and significantly enhanced outdoor wind comfort and pedestrian safety. This study introduces a morphology–wind environment coupling strategy from an architectural perspective to guide the design of dormitory buildings in cold regions. Rather than focusing on mathematical modeling, the research emphasizes design-oriented outcomes aimed at informing and optimizing practical architectural solutions for safer, more comfortable, and energy-efficient campus living environments. Full article

Since pre-industrial times, anthropogenic methane emissions have increased and are partly responsible for a changing global climate. Natural gas and oil extraction activities are one significant source of anthropogenic methane. While methods have been developed and refined to quantify onshore methane emissions, the ability of methods to directly quantify emissions from offshore production facilities remains largely unknown. Here, we review recent studies that have directly measured emissions from offshore production facilities and critically evaluate the suitability of these measurement strategies for emission quantification in a marine environment. The average methane emissions from production platforms measured using downwind dispersion methods were 32 kg h⁻¹ from 188 platforms; 118 kg h⁻¹ from 104 platforms using mass balance methods; 284 kg h⁻¹ from 151 platforms using aircraft remote sensing; and 19,088 kg h⁻¹ from 10 platforms using satellite remote sensing. Upon review of the methods, we suggest the unusually large emissions, or zero emissions observed could be caused by the effects of a decoupling of the marine boundary layer (MBL). Decoupling can happen when the MBL becomes too deep or when there is cloud cover and results in a stratified MBL with air layers of different depths moving at different speeds. Decoupling could cause: some aircraft remote sensing observations to be biased high (lower wind speed at the height of the plume); the mass balance measurements to be biased high (narrow plume being extrapolated too far vertically) or low (transects miss the plume); and the downwind dispersion measurements much lower than the other methods or zero (plume lofting in a decoupled section of the boundary layer). To date, there has been little research on the marine boundary layer, and guidance on when decoupling happens is not currently available. We suggest an offshore controlled release program could provide a better understanding of these results by explaining how and when stratification happens in the MBL and how this affects quantification methodologies. Full article

Rural villages function as relatively self-sustained production and living units with well-developed infrastructure. In this setting, investigating the transmission pathways of airborne biological particles, including pathogenic microorganisms, is pivotal for ensuring the health of residents. This study investigated the sources and dispersion of biogenic particulate matter in rural ambient air and factors influencing their behavior. Potential bioaerosol sources including livestock farming areas, composting sites, garbage dumps, and sewage treatment facilities were investigated using a calibrated portable bioaerosol detector to collect and analyze the dispersion of bioaerosol particles. The dispersal characteristics of Enterobacteriaceae were explored using an Andersen six-stage sampler. Livestock farming areas were the primary source of bioparticles. The distribution of the bioparticles varied significantly with environmental conditions. Key factors influencing their distribution included the dispersal capabilities due to wind speed and the processes of aggregation and coagulation of particles. The dispersal pathway of Enterobacteriaceae indicated that the inhabitants of residences near the dispersion source might be exposed to health risks from pathogenic bacteria present in bioparticles indoors. Understanding such characteristics and transmission patterns of bioparticles in rural environments provides a scientific basis for risk assessment and management strategies, with important implications for improving air-quality monitoring, public health policies, and environmental management in rural areas. Full article

Benzo[a]pyrene is an important indicator of polycyclic aromatic hydrocarbons pollution that exhibits complex atmospheric dynamics influenced by meteorological factors and suspended particulate matter (SPM). Herein, the factors influencing B(a)P concentration were elucidated by analyzing the monthly environmental data for Kyoto, Japan, from 2001 to 2021 using an improved association rule algorithm. Results revealed that B(a)P concentrations were 1.3–3 times higher in cold seasons than in warm seasons and SPM concentrations were lower in cold seasons. The clustering performance was enhanced by optimizing the K-means method using the sum of squared error. The efficiency and reliability of the traditional Apriori algorithm were enhanced by restructuring its candidate itemset generation process, specifically by (1) generating C₂ exclusively from frequent itemset L₁ to avoid redundant database scans and (2) implementing the iterative pruning of nonfrequent subsets during L_k → C_k+1 transitions, adding the lift parameter, and eliminating invalid rules. Strong association rules revealed that B(a)P concentrations ≤ 0.185 ng/m³ were associated with specific meteorological conditions, including humidity ≤ 58%, wind speed ≥ 2 m/s, temperature ≥ 12.3 °C, and pressure ≤ 1009.2 hPa. Among these, changes in pressure had the most substantial impact on the confidence of the association rules, followed by humidity, wind speed, and temperature. Under the influence of high SPM concentrations, favorable meteorological conditions further accelerated pollutant dispersion. B(a)P concentration increased with increasing pressure, decreasing temperature, and decreasing wind speed. Principal component analysis confirmed the robustness and accuracy of our optimized association rule approach in quantifying complex, nonlinear relationships, while providing granular, interpretable insights beyond the traditional methods. Full article