Search Results (264)

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

Turbulent diffusion plays a critical role in atmospheric pollutant dispersion, particularly in complex environments such as urban areas. This study proposes a novel theoretical approach to enhance the calculation of the turbulent diffusion coefficient in pollutant dispersion models. We propose a new expression for the turbulent diffusion coefficient (KC), which incorporates both hydrodynamic and turbulence-related time scales. This formulation links the turbulent diffusion coefficient to pollutant travel time and turbulence intensity, offering more accurate predictions of pollutant concentration distributions. By addressing the limitations of existing empirical models, this approach improves the parameterization of turbulence and reduces uncertainties in predicting maximum individual exposure under various atmospheric conditions. The study presents a theoretical model designed to advance the current understanding of atmospheric dispersion modeling. Experimental validation, while recommended, is beyond the scope of this work and is suggested as a direction for future empirical research to confirm the practical utility of the model. This theoretical formulation could be integrated into urban air quality management frameworks, providing improved estimations of pollutant peaks in complex environments. Full article

Source inversion optimization using sensor observations is a key method for rapidly and accurately identifying unknown source parameters (source strength and location) in abrupt hazardous gas leaks. Sensor number and location distribution both play important roles in source inversion; however, their combined impacts on source inversion optimization remain poorly understood. In our study, the optimization inversion method is established based on the Gaussian plume model and the generation algorithm. A research strategy combining random sampling and coefficient of variation methods was proposed to simultaneously quantify their combined impacts in the case of a single emission source. The sensor layout impact difference was analyzed under varying atmospheric conditions (unstable, neutral, and stable) and source location information (known or unknown) using the Prairie Grass experiments. The results indicated that adding sensors improved the source strength estimation accuracy more when the source location was known than when it was unknown. The impacts of sensor location distribution were strongly negatively correlated (r ≤ −0.985) with the number of sensors across scenarios. For source strength estimation, the impacts of the sensor location distribution difference decreased non-linearly with more sensors for known locations but linearly for unknown ones. The impacts of sensor number and location distribution on source strength estimation were amplified under stable atmospheric conditions compared to unstable and neutral conditions. The minimum number of randomly scattered sensors required for stable source strength inversion accuracy was 11, 12, and 17 for known locations under unstable, neutral, and stable atmospheric conditions, respectively, and 24, 9, and 21 for unknown locations. The multi-layer arc distribution outperformed rectangular, single-layer arc, and downwind-axis distributions in source strength estimation. This study enhances the understanding of factors influencing source inversion optimization and provides valuable insights for optimizing sensor layouts. Full article

This article discusses and compares approaches to the visualization of the danger zone formed as a result of spreading toxic substances during a fire at an industrial enterprise, to create predictive models and scenarios for evacuation and environmental protection measures. The purpose of this study is to analyze the features and conditions for the application of algorithms for predicting the spread of a danger zone, based on the Gauss equation and the probabilistic algorithm of a cellular automaton. The research is also aimed at the analysis of the consequences of a fire at an industrial enterprise, taking into account natural and climatic conditions, the development of the area, and the scale of the fire. The subject of this study is the development of software and algorithmic support for the visualization of the danger zone and analysis of the consequences of a fire, which can be confirmed by comparing a computational experiment and actual measurements of toxic substance concentrations. The main research methods include a Gaussian model and probabilistic, frontal, and empirical cellular automation. The results of the study represent the development of algorithms for a cellular automation model for the visual forecasting of a dangerous zone. They are characterized by taking into consideration the rules for filling the dispersion ellipse, as well as determining the effects of interaction with obstacles, which allows for a more accurate mathematical description of the spread of a cloud of toxic combustion products in densely built-up areas. Since the main problems of the cellular automation approach to modeling the dispersion of pollutants are the problems of speed and numerical diffusion, in this article the frontal cellular automation algorithm with a 16-point neighborhood pattern is used, which takes into account the features of the calculation scheme for finding the shortest path. Software and algorithmic support for an integrated system for the visualization and analysis of fire consequences at an industrial enterprise has been developed; the efficiency of the system has been confirmed by computational analysis and actual measurement. It has been shown that the future development of the visualization of dangerous zones during fires is associated with the integration of the Bayesian approach and stochastic forecasting algorithms based on Markov chains into the simulation model of a dangerous zone for the efficient assessment of uncertainties associated with complex atmospheric processes. Full article

Tyre wear particles (TWPs), generated from tyre-road abrasion, are a pervasive and under-regulated environmental pollutant, accounting for a significant share of global microplastic contamination. Recent estimates indicate that 1.3 million metric tons of TWPs are released annually in Europe, dispersing via atmospheric transport, stormwater runoff, and sedimentation to contaminate air, water, and soil. TWPs are composed of synthetic rubber polymers, reinforcing fillers, and chemical additives, including heavy metals such as zinc (Zn) and copper (Cu) and organic compounds like polycyclic aromatic hydrocarbons (PAHs) and N-(1,3-dimethylbutyl)-N′-phenyl-p-phenylenediamine (6PPD). These constituents confer persistence and bioaccumulative potential. While TWP toxicity in aquatic systems is well-documented, its ecological impacts on terrestrial environments, particularly in agricultural soils, remain less understood despite global soil loading rates exceeding 6.1 million metric tons annually. This review synthesizes global research on TWP sources, environmental fate, and ecotoxicological effects, with a focus on soil–plant systems. TWPs have been shown to alter key soil properties, including a 25% reduction in porosity and a 20–35% decrease in organic matter decomposition, disrupt microbial communities (with a 40–60% reduction in nitrogen-fixing bacteria), and induce phytotoxicity through both physical blockage of roots and Zn-induced oxidative stress. Human exposure occurs through inhalation (estimated at 3200 particles per day in urban areas), ingestion, and dermal contact, with epidemiological evidence linking TWPs to increased risks of respiratory, cardiovascular, and developmental disorders. Emerging remediation strategies are critically evaluated across three tiers: (1) source reduction using advanced tyre materials (up to 40% wear reduction in laboratory tests); (2) environmental interception through bioengineered filtration systems (60–80% capture efficiency in pilot trials); and (3) contaminant degradation via novel bioremediation techniques (up to 85% removal in recent studies). Key research gaps remain, including the need for long-term field studies, standardized mitigation protocols, and integrated risk assessments. This review emphasizes the importance of interdisciplinary collaboration in addressing TWP pollution and offers guidance on sustainable solutions to protect ecosystems and public health through science-driven policy recommendations. Full article

This study presented a comprehensive physical–statistical analysis of atmospheric particulate matter (PM₁₀ and PM_2.5) and trace gases (SO₂ and O₃) over Faial Island in the Azores archipelago during 2024. We collected real-time data at the Espalhafatos rural background station, covering 35,137 observations per pollutant, with 15 min intervals. Descriptive statistics, probability distribution fitting (Normal, Lognormal, Weibull, Gamma), and correlation analyses were employed to characterize pollutant dynamics and identify extreme pollution episodes. The results revealed that PM_2.5 (fine particles) concentrations are best modeled by a Lognormal distribution, while PM₁₀ concentrations fit a Gamma distribution, highlighting the presence of heavy-tailed, positively skewed behavior in both cases. Seasonal and episodic variability was significant, with multiple Saharan dust transport events contributing to PM exceedances, particularly during winter and spring months. These events, confirmed by CAMS and SKIRON dust dispersion models, affected not only southern Europe but also the Northeast Atlantic, including the Azores region. Weak to moderate correlations were observed between PM concentrations and meteorological variables, indicating complex interactions influenced by atmospheric stability and long-range transport processes. Linear regression analyses between SO₂ and O₃, and between SO₂ and PM_2.5, showed statistically significant but low-explanatory relationships, suggesting that other meteorological and chemical factors play a dominant role. This result highlights the importance of developing air quality policies that address both local emissions and long-range transport phenomena. They support the implementation of early warning systems and health risk assessments based on probabilistic modeling of particulate matter concentrations, even in remote Atlantic locations such as the Azores. 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

This study examines the assessment and management strategies for odor risks during emergency cleanup of VOC-contaminated waste. By analyzing illegally dumped VOC waste, the impact on odor intensity levels and exceedance probabilities in nearby residential areas was evaluated. Utilizing a VOC source emission model, a Gaussian plume dispersion model, and Monte Carlo simulations under various meteorological conditions, the effectiveness of the control measures was assessed. Key pollutants included ethylbenzene, toluene, styrene, and m/p-xylene, which, despite posing minimal short-term health risks (PHI: 0.17–0.64), exhibited significant odor risks (Odor PHI: 127–1156). At 20 m from the source, the probability of the odor intensity exceeding Level 2.5 approached 100%, decreasing to 85% at 50 m and further declining with distance. Atmospheric stability shifts—from very unstable (Class A) to stable (Class F)—increased the odor intensity from 0.5 to 2.5. Under moderately stable conditions (Class E), m/p-xylene had a 44.2% probability of exceeding an odor intensity level of 2.5. Even at 250 m, the odor intensity levels ranged between 1.2 and 1.7, remaining perceptible. Effective mitigation strategies include establishing appropriate buffer distances and using adsorption materials like activated carbon. Full article

The objective of this article is to conduct a review to analyze global trends in the use of air pollution models under the influence of climate variability (CV) over urban areas. Five scientific databases were used (2013–2024): Scopus, ScienceDirect, SpringerLink, Web of Science, and Google Scholar. The frequency of citations of the variables of interest in the selected scientific databases was analyzed by means of an index using quartiles (Q). The results showed a hierarchy in the use of models: regional climate models/RCMs (Q3) > statistical models/SMs (Q3) > chemical transport models/CTMs (Q4) > machine learning models/MLMs (Q4) > atmospheric dispersion models/ADMs (Q4). RCMs, such as WRF, were essential for generating high-resolution projections of air pollution, crucial for local impact assessments. SMs, such as GAM, excelled in modeling nonlinear relationships between air pollutants and climate variables. CTMs, such as WRF-Chem, simulated detailed atmospheric chemical processes vital for understanding pollutant formation and transport. MLMs, such as ANNs, improved the accuracy of predictions and uncovered complex patterns. ADMs, such as HYSPLIT, evaluated air pollutant dispersion, informing regulatory strategies. The most studied pollutants globally were O₃ (Q3) > PM (Q3) > VOCs (Q4) > NOx (Q4) > SO₂ (Q4), with models adapting to their specific characteristics. Temperature emerged as the dominant climate variable, followed by wind, precipitation, humidity, and solar radiation. There was a clear differentiation in the selection of models and variables between high- and low-income countries. CTMs predominated in high-income countries, driven by their ability to simulate complex physicochemical processes, while SMs were preferred in low-income countries, due to their simplicity and lower resource requirements. Temperature was the main climate variable, and precipitation stood out in low-income countries for its impact on PM removal. VOCs were the most studied pollutant in high-income countries, and NOx in low-income countries, reflecting priorities and technical capabilities. The coupling between regional atmospheric models and city-scale air quality models was vital; future efforts should emphasize intra-urban models for finer urban pollution resolution. This study highlights how national resources and priorities influence air pollution research over cities under the influence of CV. Full article

Gases emitted from active volcanic systems constitute a primary natural source of global atmospheric pollution. Atmospheric sulfur dioxide (SO₂) concentrations were monitored using a near-continuous network based on Scan-DOAS (Differential Optical Absorption Spectroscopy) technology. Complementary intermittent measurements were performed using a UV Thermo^® analyzer deployed at fixed locations and along predefined transects on the island. SO₂ flux data derived from the Scan-DOAS measurements, coupled with atmospheric dispersion maps generated using the AERMOD modeling software, enabled the estimation of SO₂ distribution across the volcanic crater region and inhabited areas of the island, including Vulcano Village and Vulcano Piano. The results of the estimation of SO₂ concentration in the atmosphere, integrated with the dispersion modeling, exhibited consistency with direct SO₂ concentration measurements obtained by the Thermo^® analyzer, demonstrating coherence between the two methodologies, although some overestimations of ambient SO₂ were noted. This study provided valuable insights into areas with anomalous SO₂ concentrations exceeding the threshold limits established by the World Health Organization (WHO) and the European Union (EU). These limits are generally exceeded in the crater zone and surrounding areas. The findings also highlighted the influence of prevailing winds and the temporal variations in volcanic degassing activity observed over the preceding 17 years, characterized by four periods of unrest degassing with SO₂ emission rates from the summit solfataric area reaching up to 250 tonnes per day (td⁻¹). Full article

This study investigates the impact of a thermal power plant site on air quality in Bucharest, Romania. It emphasizes the importance of accurate air pollutant inmission measurements in urban areas by utilizing mobile measurements of low-cost sensors, Copernicus’ Copernicus Atmosphere Monitoring Service (CAMS) and Copernicus Land Monitoring Service (CLMS), and satellite retrieval to better understand climate change drivers and their potential impact on near- surface concentrations and column densities of ${\mathrm{NO}}_2$, CO, and PM (particulate matter). It focuses the attention on the need of considering the placement of power plants in relation to metropolitan areas while making this assessment. The research highlights the limits of typical mesoscale air quality models in effectively capturing pollution dispersion and distribution using LUR (Land Use Regressions) retrievals. The authors investigate a variety of ways to better understand air pollution in metropolitan areas, including satellite observations, mobile measurements, and land use regression models. The study focuses largely on Bucharest, the capital of Romania, which has air pollution issues caused by vehicle traffic, industrial activity, heating systems, and power plants. The results indicate how the placement of a power plant may affects air quality in the nearby residential areas. Full article

Nitrogen dioxide (NO₂) is a major air pollutant in urban areas, and achieving good accuracy and sensitivity in low-cost measurements is desirable to monitor NO₂ levels in settings with high spatio-temporal variability. This paper describes a ratiometric approach that uses the different absorption at two nearby wavelengths to quantify NO₂. The response to NO₂ and other potential interferences is calculated at 437.3 and 439.4 nm for a low-resolution (1.44 nm) system. Owing to its elevated concentration and strong absorption compared to other absorbing gases, NO₂ dominates the ratio of light absorption at these wavelengths in urban settings. The approach is experimentally demonstrated in a simple measurement system comprising a blue LED, narrow bandpass filters and non-dispersive detectors. The approach was validated in atmospheric simulation chamber experiments over an 8 m pathlength and achieved a high level of agreement against a reference DOAS spectral analysis (R² = 0.97). Mixing ratios of up to 12 ppm were measured with a standard deviation of 51 ppb, suggesting that low ppb-level sensitivity can be achieved in pathlengths of a few hundred metres. The spectral stability of the ratiometric method was demonstrated in the open atmosphere using a short open-path system with a pathlength of 45 m. The standard deviation of the ratio of intensities in the two channels was 0.2%, despite changes in the transmitted intensity of almost 90%. The ratiometric two-channel approach developed in this work can be used in both in situ and remote sensing configurations, and we suggest that it has potential for use in a range of settings, including for low-cost monitoring in low-income cities and towns and continuous emission monitoring. Full article

Nowadays, industries and society are very concerned about pollution, well-being, health, air quality, and the possible negative effects of industrial emissions on a property’s surroundings. This gas dispersion is typically estimated with Gaussian Plume/Puff Models or software that uses these models with slight adjustments. The issue regarding these models is that they do not consider the surroundings’ particularities, for instance, when obstacles are present, and they require experimental data to adapt to specific scenarios. Therefore, the aim of this work is to validate the use of ANSYS Fluent^® 2022 R1 for modelling atmospheric gas dispersion. This validation is performed by comparing the ANSYS Fluent^® 2022 R1 findings to published experimental data, Gaussian Plume Models (GPM in this case corresponds to the application of the Gaussian Equation or Gaussian Fit, and does not correspond to a specific dispersion model), and ALOHA 5.4.7 software. A comparison between these three alternatives was not available in the literature. In terms of downwind dispersion, the findings of the three models are extremely comparable. However, ANSYS Fluent^® has a propensity to overestimate the concentration at higher heights. Validation using ANSYS Fluent^® in atmospheric gas dispersion applications enables confident results to be obtained in other scenarios. Differences in pollutant estimation between models are clear when studying more complex cases containing turbulence-inducing geometries. In these cases, CFD exhibits a more realistic description of the transport phenomena than the other models considered. The Prairie Grass Project is used as a tool to validate the CFD model, and to demonstrate its potential for more complex cases. 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

This study focuses on the nameplate of Vila D. Bosco, a modern building in Macau from the time of Portuguese rule, and looks at the types of metal materials and surface coatings used, as well as how they corrode due to the tropical marine climate affecting the building’s metal parts. The study uses different techniques, such as X-ray fluorescence spectroscopy (XRF), scanning electron microscopy/energy dispersive spectroscopy (SEM-EDS), X-ray diffraction (XRD), attenuated total internal reflectance Fourier transform infrared spectroscopy (ATR-FTIR), and cross-sectional microscopic analysis, to carefully look at the metal, corrosion products, and coating of the nameplate. The results show that (1) the nameplate matrix is a resulfurized steel with a high sulfur content (Fe up to 97.3% and S up to 1.98%), and the sulfur element is evenly distributed inside, which is one of the internal factors that induce corrosion. (2) Rust is composed of polycrystalline iron oxides such as goethite (α-FeOOH), hematite (α-Fe₂O₃), and magnetite (Fe₃O₄) and has typical characteristics of atmospheric oxidation. (3) The white and yellow-green coatings on the nameplate are oil-modified alkyd resin paints, and the color pigments are TiO₂, PbCrO₄, etc. The surface layer of the letters is protected by a polyvinyl alcohol layer. The paint application process leads to differences in the thickness of the paint in different regions, which directly affects the anti-rust performance. The study reveals the deterioration mechanism of resulfurized steel components in a subtropical polluted environment and puts forward repair suggestions that consider both material compatibility and reversibility, providing a reference for the protection practice of modern and contemporary architectural metal heritage in Macau and even in similar geographical environments. Full article