Search Results (64)

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

Reliable source term prediction for hazardous pollutant puffs in urban microenvironments is challenging, especially for risk management under strict time constraints. Puff movement is highly stochastic due to atmospheric turbulence, intensified by complex urban canopies. This complexity, combined with time limitations, makes advanced computational modeling impractical. A more efficient approach is leveraging past and present data using Machine Learning (ML) techniques. This study proposes an ML-based method, enriched with simplified physical modeling, for source term estimation of unforeseen hazardous air releases in monitored urban areas. The Random Forest Regression, commonly used in meteorology and air quality studies, has been selected. A novel variable selection method is introduced, including the following: (a) a model-derived Exposure Burden Index (EBI) reflecting plume–morphology interactions; (b) a plume travel time indicator; (c) the standard deviation of input variables capturing stochastic behavior; and (d) the total dosage-to-mass released ratio at sensor locations as the target variable. The case study examines JU2003 field experiments involving SF₆ puffs released at street level in Oklahoma City’s urban core, a challenging scenario due to the limited number of sensors and historical data. Results demonstrate the approach’s effectiveness, offering a promising, realistic alternative to traditional computationally intensive methods. Full article

Urban vitality is a critical indicator of both urban sustainability and quality of life. However, comprehensive studies examining the threshold effects and interaction mechanisms of built environment factors on urban vitality at the block level remain limited. This study proposed to develop a comprehensive framework for urban vitality by incorporating multi-source data, and the central urban area of Xi’an, China, was selected as the study area. Four machine learning models, LightGBM, XGBoost, GBDT, and random forest, were employed to identify the most fitted model for analyzing threshold effects and interactions among built environment factors on shaping urban vitality. The results showed the following: (1) Xi’an’s urban vitality exhibited a distinct gradient, with the highest vitality concentrated in the Yanta District; (2) life service facility density was the most significant determinant of vitality (19.91%), followed by air quality (9.01%) and functional diversity (6.49%); and (3) significant interactions among built environment factors were observed. In particular, streets characterized by both high POI diversity (greater than 0.8) and low PM_2.5 concentrations (below 48.5 μg/m³) exhibited notably enhanced vitality scores. The findings of this study provide key insights into strategies for boosting urban vitality, offering actionable insights for improving land use allocations and enhancing quality of life. Full article

During the intense heatwaves of late summer 2024, Washington, D.C.’s urban landscape revealed the powerful influence of urban morphology on microclimates and air quality. This study investigates the impact of building height-to-width (H/W) ratios on the urban heat island (UHI) effect, using a combination of field measurements and Computational Fluid Dynamics (CFD) simulations to understand the dynamics. Street-level data collected from late August to November 2024 across three sites in Washington, D.C., indicate that high H/W ratios (1.5–2.0) increased temperatures by approximately 2–3 °C and reduced wind speeds to around 0.8 m/s. These conditions led to elevated pollutant concentrations, with ozone (O₃) ranging from 1.8 to 7.3 ppb, nitrogen dioxide (NO₂) from 0.3 to 0.5 ppm, and carbon monoxide (CO) remaining relatively constant at approximately 2.1 ppm. PM_2.5 concentrations fluctuated between 2.8 and 0.4 $\mathsf{\mu }$g/m³. Meanwhile, lower H/W ratios (less than 1.5) demonstrated better air circulation and lower pollution levels. The CFD simulations are in agreement with the experimental data, yielding an RMSE of 0.75 for temperature, demonstrating its utility for forecasting UHI effects under varying urban layouts. These results demonstrate the potential of Computational Fluid Dynamics in not only modeling but also predicting UHI dynamics. Full article

The assessment of air pollution is an important and relevant issue that requires continuous monitoring and control, especially in urban spaces. However, using instrumental air quality measurement techniques and deploying meters throughout the city is extremely expensive, so a biological alternative can be used—a bioindicator, i.e., a species whose vital functions or morphological structure can reveal the qualitative state of the environment. In this work, the lichen Hypogymnia physodes L. was used to analyze air pollution in areas of the provincial city of Opole, southern Poland. Microscope and chemotaxonomy methods were used in the laboratory to confirm field identification of lichens (atlases and keys). The selected elements, Mn, Fe, Ni, Cu, Zn, Cd, and Pb, were determined using atomic absorption spectrometry, and direct mercury analyzer was used to analyzed Hg concentration. Factor analysis (FA) was performed to associate elements with possible sources of air pollution. The highest concentrations of analytes were found at measurement points close to railway roads (Fe = 5131 mg/kg) and streets with heavy traffic (Pb = 101 mg/kg). Statistically significant differences (p < 0.001) were found between the concentrations of individual elements, which have positive correlation coefficients higher than 0.65. Based on the research carried out, different anthropogenic and traffic-related activities can be considered as one of the main sources of air pollution in Opole City based on the results of FA. Using an additional lichen scale, it can be concluded that the areas surveyed in the town of Opole can be classified as zone IV—characterized by an increase in the number of leaf lichens (additionally co-occurring lichens of the Polycauliona candelaria species), i.e., an area with an average level of air pollution (based also on contamination factor [CF] and pollution load index [PLI]). Accumulation concentrations of heavy metals in lichen were metal-specific and varied spatially, thus reflecting local differences in heavy metal deposition. The research presented here proves that low-cost passive biomonitoring can effectively support classical methods of assessing air pollution in urban spaces. Full article

Being an essential issue in global climate warming, the response of urban green spaces to air pollution and climate variability because of rapid urbanization has become an increasing concern at both the local and global levels. This study explored the response of urban vegetation to air pollution and climate variability in the Bucharest metropolis in Romania from a spatiotemporal perspective during 2000–2024, with a focus on the 2020–2024 period. Through the synergy of time series in situ air pollution and climate data, and derived vegetation biophysical variables from MODIS Terra/Aqua satellite data, this study applied statistical regression, correlation, and linear trend analysis to assess linear relationships between variables and their pairwise associations. Green spaces were measured with the MODIS normalized difference vegetation index (NDVI), leaf area index (LAI), photosynthetically active radiation (FPAR), evapotranspiration (ET), and net primary production (NPP), which capture the complex characteristics of urban vegetation systems (gardens, street trees, parks, and forests), periurban forests, and agricultural areas. For both the Bucharest center (6.5 km × 6.5 km) and metropolitan (40.5 km × 40.5 km) test areas, during the five-year investigated period, this study found negative correlations of the NDVI with ground-level concentrations of particulate matter in two size fractions, PM2.5 (city center r = −0.29; p < 0.01, and metropolitan r = −0.39; p < 0.01) and PM10 (city center r = −0.58; p < 0.01, and metropolitan r = −0.56; p < 0.01), as well as between the NDVI and gaseous air pollutants (nitrogen dioxide—NO₂, sulfur dioxide—SO₂, and carbon monoxide—CO. Also, negative correlations between NDVI and climate parameters, air relative humidity (RH), and land surface albedo (LSA) were observed. These results show the potential of urban green to improve air quality through air pollutant deposition, retention, and alteration of vegetation health, particularly during dry seasons and hot summers. For the same period of analysis, positive correlations between the NDVI and solar surface irradiance (SI) and planetary boundary layer height (PBL) were recorded. Because of the summer season’s (June–August) increase in ground-level ozone, significant negative correlations with the NDVI (r = −0.51, p < 0.01) were found for Bucharest city center and (r = −76; p < 0.01) for the metropolitan area, which may explain the degraded or devitalized vegetation under high ozone levels. Also, during hot summer seasons in the 2020–2024 period, this research reported negative correlations between air temperature at 2 m height (TA) and the NDVI for both the Bucharest city center (r = −0.84; p < 0.01) and metropolitan scale (r = −0.90; p < 0.01), as well as negative correlations between the land surface temperature (LST) and the NDVI for Bucharest (city center r = −0.29; p< 0.01) and the metropolitan area (r = −0.68, p < 0.01). During summer seasons, positive correlations between ET and climate parameters TA (r = 0.91; p < 0.01), SI (r = 0.91; p < 0.01), relative humidity RH (r = 0.65; p < 0.01), and NDVI (r = 0.83; p < 0.01) are associated with the cooling effects of urban vegetation, showing that a higher vegetation density is associated with lower air and land surface temperatures. The negative correlation between ET and LST (r = −0.92; p < 0.01) explains the imprint of evapotranspiration in the diurnal variations of LST in contrast with TA. The decreasing trend of NPP over 24 years highlighted the feedback response of vegetation to air pollution and climate warming. For future green cities, the results of this study contribute to the development of advanced strategies for urban vegetation protection and better mitigation of air quality under an increased frequency of extreme climate events. Full article

This study examines how urban morphology, road configurations, and meteorological factors shape fine particulate matter (PM_2.5) dispersion in high-density urban environments, addressing a gap in block-level air quality analysis. While previous research has focused on individual street canyons, this study highlights the broader influence of building arrangement and height. Integrating computational fluid dynamics (CFD) simulations with interpretable machine learning (ML) models quantifies PM_2.5 concentrations across various urban configurations. CFD simulations were conducted on different road layouts, block height configurations, and aspect ratio (AR) levels. The resulting dataset trained five ML models with Extreme Gradient Boosting (XGBoost), achieving the highest accuracy (91–95%). Findings show that road-specific mitigation strategies must be tailored. In loop-road networks, centrally elevated buildings enhance ventilation, while in grid-road networks, taller perimeter buildings shield inner blocks from arterial emissions. Additionally, this study identifies a threshold effect of AR, where values exceeding 2.5 improve PM_2.5 dispersion under high wind velocity. This underscores the need for wind-sensitive designs, including optimized wind corridors and building alignments, particularly in high-density areas. The integration of ML with CFD enhances predictive accuracy, supporting data-driven urban planning strategies to optimize road layouts, zoning regulations, and aerodynamic interventions for improved air quality. Full article

Densely populated urban areas often experience poor air quality due to high levels of anthropogenic emissions. The population is frequently exposed to harmful gaseous and particulate pollutants, which are directly linked to various health issues, including respiratory diseases. Accurately assessing and predicting pollutant concentrations within urban areas is therefore crucial. This study developed a computational fluid dynamic (CFD) model designed to capture turbulence effects that influence pollutant dispersion in urban environments. The focus was on key pollutants commonly associated with vehicular emissions, such as carbon monoxide (CO), nitrogen oxides (NO_x), volatile organic compounds (VOCs), and particulate matter (PM). The model was applied to the city of Augsburg, Germany, to simulate pollutant behavior at a microscale level. The primary objectives were twofold: first, to accurately predict local pollutant concentrations and validate these predictions against measurement data; second, to evaluate the representativeness of air quality monitoring stations in reflecting the broader pollutant distribution in their vicinity. The approach presented here has demonstrated that when focusing on an area within a specific radius of an air quality station, the representativeness ranges between 10% and 16%. On the other hand, when assessing the representativeness across the street of deployment, the spatial coverage of the sensor ranges between 23% and 80%. This analysis highlights that air quality stations primarily capture pollution levels from high-activity areas directly across their deployment site, rather than reflecting conditions in nearby lower-activity zones. This approach ensures a more comprehensive understanding of urban air pollution dynamics and assesses the reliability of air quality (AQ) monitoring stations. Full article

Transport areas in urban environments typically cover 10–20% of a city’s area. Due to their hierarchical structure and network layout, they present a unique opportunity to integrate Nature-based Solutions (NbSs) within cities strategically. In Poland, however, the current use of NbSs in streetscapes tends to be sporadic, localized, and often resulting from grassroots initiatives. This study aimed to assess how much the provision of ecosystem services (ESs) in cities depends on and can be enhanced by NbSs. To explore this, simulations were conducted using six NbSs scenarios, selected based on an analysis of solutions specifically designed for streets and their characteristics. This research focused on a densely built and populated district of Warsaw. The findings revealed that applying NbSs can significantly reduce stormwater runoff, increase carbon sequestration, and improve air quality. The level of ES provision depends on the solutions used, with the introduction of woody vegetation, particularly tall shrubs and trees, proving most effective. The results show that the scenario-based approach allows for flexible streetscape design, enabling the application of individually selected NbSs. Moreover, the approach helps to select optimal elements that enhance the provision of ES crucial to adapting cities to climate change. Full article

This paper proposes a novel model to determine occupancy density for outdoor events to prevent infectious disease transmission caused by the impossibility of proper dilution of human effluents in the atmosphere. It uses standardization processes to calculate natural ventilation air renewal and establishes theoretical occupancy based on activity and exhaled air percentage, aiming for indoor air quality comparable to the IDA2 standards. The study focuses on mass events in Mostoles (Spain), analyzing street activities and bullring events. It found that above a certain height in the open air, infection risk is low, eliminating capacity limitations. The resulting mathematical expressions can be adapted to different pathogens, ensuring the quality of indoor air conditions through capacity control. The process determines the ventilation required based on physical activity, considering both unrestricted and restricted situations. The relationship between required and available ventilation prevents disease transmission. The method’s effectiveness is demonstrated through comparisons between estimates and environmental measurements during Mostoles events. The maximum outdoor occupancy at ground level to achieve air quality comparable to the IDA2 standards is determined to be 2.36 persons/m², while to prevent the transmission of SARS-CoV-2 it is determined to be 1.98 persons/m². In addition, transmission will not occur during mass gatherings in locations over five meters above ground level. In conclusion, this model provides an adaptable tool to prevent the spread of infectious diseases at outdoor events by ensuring adequate air quality through occupancy control. Full article

Green infrastructure, such as street trees, can help improve air quality, but the role of rainfall in cleaning total particulate matter (TPM) from tree leaves is not well understood, especially in cities like Santiago, Chile. This study measured TPM deposited on leaves and its elemental composition of two native tree species, Quillaja saponaria and Schinus molle, by five independent rainfall episodes. The results showed significant differences in how each tree species responded to rainfall. The total amount of TPM finally removed or retained at the leaf level in the five rainfall events studied was 4.72 and 8.43 mg/g_ldw for Q. saponaria and S. molle, respectively. Q. saponaria decreased TPM levels after rainfall, while S. molle exhibited mixed responses, increasing or decreasing TPM accumulation on leaves after different intensities of rainfalls. Elemental analysis revealed metals such as lithium and nickel—potentially linked to electric vehicle batteries—and tin and antimony–potentially linked to industrial processes. Rainfall benefited air quality by removing heavy metals from the atmosphere and aiding plant recovery from TPM accumulation. However, further research is needed on metal speciation in TPM and its foliar uptake by plants. This study provides some insights into the complex interactions between trees leaves, TPM deposition, and rainfall. Full article

Adapting to climate change and controlling carbon emissions have emerged as significant challenges faced by the international community. The high-quality pedestrian space system of TOD blocks, as an important means for carbon reduction and carbon sink increase in cities, showcases the effect of green intensification and low-carbon sustainable urban space development. In this study, by combining the research on low-carbon block creation and urban microclimate, focusing on the technical process of the three stages of pre-treatment, core calculation, and post-treatment, comprehensively considering the three elements of microclimate, namely wind, heat, and carbon, and their influencing parameters, and introducing a CFD simulation method for porous media, a CFD simulation technology framework for microclimate improvement in urban design is constructed. Through the spatial visualization of the software solution calculation results and the correlation and comparative analysis of the measured data, we quantitatively analyze the coupling relationship between the block morphology and the comprehensive environment of wind, heat, and carbon. The research results indicate that by rationally adjusting indicator elements such as the height-to-width ratio of streets and entrance forms, it is possible to effectively facilitate cooling, ventilation, and air circulation within blocks and dilute the CO₂ concentration. Finally, from the urban design element systems at the micro, meso, and macro levels, the adaptive planning strategies in the three dimensions of the spatial form, constituent elements, and planning guidelines of TOD blocks are summarized and refined, with the aim of achieving the low-carbon transformation of cities through the creation of a healthy microclimate environment. Full article

Air quality monitoring networks regulated by law provide accurate but sparse measurements of PM2.5 mass concentrations. High-spatial resolution maps of the PM2.5 mass concentration values are necessary to better estimate the citizen exposure to outdoor air pollution and the sanitary consequences. To address this, a field campaign was conducted in Teltow, a midsize city southwest of Berlin, Germany, for the 2021–2023 period. A network of optical sensors deployed by Pollutrack included fixed monitoring stations as well as mobile sensors mounted on the roofs of buses and cars. This setup provides PM2.5 pollution maps with a spatial resolution down to 100 m on the main roads. The reliability of Pollutrack measurements was first established with comparison to measurements from the German Environment Agency (UBA) and modelling calculations based on high-resolution weather forecasts. Using these validated data, maps were generated for 2023, highlighting the mean PM2.5 mass concentrations and the number of days per year above the 15 µg.m⁻³ value (the daily maximum recommended by the World Health Organization (WHO) in 2021). The findings indicate that PM2.5 levels in Teltow are generally in the good-to-moderate range. The higher values (hot spots) are detected mainly along the highways and motorways, where traffic speeds are higher compared to inner-city roads. Also, the PM2.5 mass concentrations are higher on the street than on the sidewalks. The results were further compared to those in the city of Paris, France, obtained using the same methodology. The observed parallels between the two datasets underscore the strong correlation between traffic density and PM2.5 concentrations. Finally, the study discusses the advantages of integrating such high-resolution sensor networks with modelling approaches to enhance the understanding of localized PM2.5 variability and to better evaluate public exposure to air pollution. Full article

Air pollution, including particulate matter (PM), impacts public health in urban areas. Vegetation acts as a natural filter, removing environmental pollution by absorbing large quantities of toxic substances on the foliage. Ambient air pollution problems are real in Armenia’s cities. This article presents the results of a study based on field sampling in July 2022 undertaken in urban parks and streets in the Armenian cities of Yerevan, Gyumri, and Vanadzor. The three cities have different climates and geographic conditions. The main research goal was a comparative study of the accumulation of PM by urban greenery. The most widespread tree species were selected for the study in each city: in Yerevan, Platanus orientalis and Quercus robur; in Gyumri, Fraxinus excelsior and Tilia caucasica; and in Vanadzor, Aesculus hippocastanum and Acer pseudoplatanus. The ecological status of trees was assessed through visual observation. Tree species with high PM uptake potential were identified and selected for inclusion in urban greening systems (Platanus orientalis, Fraxinus excelsior, and Quercus robur in Yerevan; Tilia caucasica, Sorbus persica, Fraxinus excelsior, and Populus alba in Grumri; Acer pseudoplatanus, Fraxinus excelsior, Aesculus hippocastanum, and Thuja occidentalis in Vanadzor.). High PM accumulation was found on the leaves of tree species in all of the investigated cities, with the largest amount recorded in Yerevan. In these cities, PM levels were higher in street plantations than in parks. All studied tree species have a high potential for PM absorption, demonstrating strong phytofilter properties. Therefore, they can be effectively used in their typical climatic zones and included in street plantings, gardens, and parks. These results can help urban planners and policymakers make informed decisions about urban greening initiatives to improve air quality and overall wellbeing. Full article

To effectively mitigate anthropogenic air pollution, it is imperative to implement strategies aimed at reducing emissions from traffic-related sources. Achieving this objective can be facilitated by employing modeling techniques to elucidate the interplay between environmental impacts and traffic activities. This paper highlights the importance of combining traffic emission models with high-resolution turbulence and dispersion models in urban areas at street canyon level and presents the development and implementation of an interface between the mesoscopic traffic and emission model MATSim and PALM-4U, which is a set of urban climate application modules within the PALM model system. The proposed coupling mechanism converts MATSim output emissions into input emission flows for the PALM-4U chemistry module, which requires translating between the differing data models of both modeling systems. In an idealized case study, focusing on Berlin, the model successfully identified “hot spots” of pollutant concentrations near high-traffic roads and during rush hours. Results show good agreement between modeled and measured NO_x concentrations, demonstrating the model’s capacity to accurately capture urban pollutant dispersion. Additionally, the presented coupling enables detailed assessments of traffic emissions but also offers potential for evaluating the effectiveness of traffic management policies and their impact on air quality in urban areas. Full article