Atmosphere

17 pages, 5004 KiB

Open AccessArticle

Local Emissions Drive Summer PM_2.5 Pollution Under Adverse Meteorological Conditions: A Quantitative Case Study in Suzhou, Yangtze River Delta

by Minyan Wu, Ningning Cai, Jiong Fang, Ling Huang, Xurong Shi, Yezheng Wu, Li Li and Hongbing Qin

Atmosphere 2025, 16(7), 867; https://doi.org/10.3390/atmos16070867 (registering DOI) - 16 Jul 2025

Abstract

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 [...] Read more.

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

(This article belongs to the Section Air Quality)

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16 pages, 5287 KiB

Open AccessArticle

Long-Term Integrated Measurements of Aerosol Microphysical Properties to Study Different Combustion Processes at a Coastal Semi-Rural Site in Southern Italy

by Giulia Pavese, Adelaide Dinoi, Mariarosaria Calvello, Giuseppe Egidio De Benedetto, Francesco Esposito, Antonio Lettino, Margherita Magnante, Caterina Mapelli, Antonio Pennetta and Daniele Contini

Atmosphere 2025, 16(7), 866; https://doi.org/10.3390/atmos16070866 (registering DOI) - 16 Jul 2025

Abstract

Biomass burning processes affect many semi-rural areas in the Mediterranean, but there is a lack of long-term datasets focusing on their classification, obtained by monitoring carbonaceous particle concentrations and optical properties variations. To address this issue, a campaign to measure equivalent black carbon [...] Read more.

Biomass burning processes affect many semi-rural areas in the Mediterranean, but there is a lack of long-term datasets focusing on their classification, obtained by monitoring carbonaceous particle concentrations and optical properties variations. To address this issue, a campaign to measure equivalent black carbon (eBC) and particle number size distributions (0.3–10 μm) was carried out from August 2019 to November 2020 at a coastal semi-rural site in the Basilicata region of Southern Italy. Long-term datasets were useful for aerosol characterization, helping to clearly identify traffic as a constant eBC source. For a shorter period, PM_2.5 mass concentrations were also measured, allowing the estimation of elemental and organic carbon (EC and OC), and chemical and SEM (scanning electron microscope) analysis of aerosols collected on filters. This multi-instrumental approach enabled the discrimination among different biomass burning (BB) processes, and the analysis of three case studies related to domestic heating, regional smoke plume transport, and a local smoldering process. The AAE (Ångström absorption exponent) daily pattern was characterized as having a peak late in the morning and mean hourly values that were always higher than 1.3. Full article

(This article belongs to the Section Aerosols)

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20 pages, 6439 KiB

Open AccessArticle

Spatiotemporal Patterns of Hongshan Culture Settlements in Relation to Middle Holocene Climatic Fluctuation in the Horqin Dune Field, Northeast China

by Wenping Xue, Heling Jin, Wen Shang and Jing Zhang

Atmosphere 2025, 16(7), 865; https://doi.org/10.3390/atmos16070865 (registering DOI) - 16 Jul 2025

Abstract

Given the increasing challenges posed by frequent extreme climatic events, understanding the climate–human connection between the climate system and the transitions of ancient civilizations is crucial for addressing future climatic challenges, especially when examining the relationship between the abrupt events of the Holocene [...] Read more.

Given the increasing challenges posed by frequent extreme climatic events, understanding the climate–human connection between the climate system and the transitions of ancient civilizations is crucial for addressing future climatic challenges, especially when examining the relationship between the abrupt events of the Holocene and the Neolithic culture development. Compared with the globally recognized “4.2 ka collapse” of ancient cultures, the initial start time and the cultural significance of the 5.5 ka climatic fluctuation are more complex and ambiguous. The Hongshan culture (6.5–5.0 ka) is characterized by a complicated society evident in its grand public architecture and elaborate high-status tombs. However, the driving mechanisms behind cultural changes remain complex and subject to ongoing debate. This paper delves into the role of climatic change in Hongshan cultural shifts, presenting an integrated dataset that combines climatic proxy records with archaeological data from the Hongshan culture period. Based on synthesized aeolian, fluvial-lacustrine, loess, and stalagmite deposits, the study indicates a relatively cold and dry climatic fluctuation occurred during ~6.0–5.5 ka, which is widespread in the Horqin dune field and adjacent areas. Combining spatial analysis with ArcGis 10.8 on archaeological sites, we propose that the climatic fluctuation between ~6.0–5.5 ka likely triggered the migration of the Hongshan settlements and adjustment of survival strategies. Full article

(This article belongs to the Special Issue Desert Climate and Environmental Change: From Past to Present)

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29 pages, 19566 KiB

Open AccessArticle

Estimating Urban Linear Heat (UHI_ULI) Effect Along Road Typologies Using Spatial Analysis and GAM Approach

by Elahe Mirabi, Michael Chang, Georgy Sofronov and Peter Davies

Atmosphere 2025, 16(7), 864; https://doi.org/10.3390/atmos16070864 (registering DOI) - 15 Jul 2025

Abstract

The urban heat island (UHI) effect significantly impacts urban environments, particularly along roads, a phenomenon known as urban linear heat (UHI_ULI). Numerous factors contribute to roads influencing the UHI_ULI; however, effective mitigation strategies remain a challenge. This study examines [...] Read more.

The urban heat island (UHI) effect significantly impacts urban environments, particularly along roads, a phenomenon known as urban linear heat (UHI_ULI). Numerous factors contribute to roads influencing the UHI_ULI; however, effective mitigation strategies remain a challenge. This study examines the relationship between canopy cover percentage, normalized difference vegetation index, land use types, and three road typologies (local, regional, and state) with land surface temperature. This study is based on data from the city of Adelaide, Australia, using spatial analysis, and statistical modelling. The results reveal strong negative correlations between land surface temperature and both canopy cover percentage and normalized difference vegetation index. Additionally, land surface temperature tends to increase with road width. Among land use types, land surface temperature varies from highest to lowest in the order of parkland, industrial, residential, educational, medical, and commercial areas. Notably, the combined influence of the road typology and land use produces varying effects on land surface temperature. Canopy cover percentage and normalized difference vegetation index consistently serve as dominant cooling factors. The results highlight a complex interplay between built and natural environments, emphasizing the need for multi-factor analyses and a framework based on the local climate and the type of roads (local, regional, and state) to effectively evaluate UHI_ULI mitigation approaches. Full article

(This article belongs to the Special Issue Advanced Studies on Climate Change in Urban Areas: Emerging Technologies and Strategies to Address Heat Waves and Improve Thermo-Hygrometric Comfort)

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19 pages, 7129 KiB

Open AccessArticle

Dendroclimatic Reconstruction of Seasonal Precipitation from Two Endangered Spruce Species in Northeastern Mexico

by Christian Wehenkel, Oscar A. Díaz-Carrillo and Jose Villanueva-Díaz

Atmosphere 2025, 16(7), 863; https://doi.org/10.3390/atmos16070863 (registering DOI) - 15 Jul 2025

Abstract

Water availability is a major constraint on socioeconomic development in northeastern Mexico, highlighting the need for effective water resource planning that accounts for the variability and extremes of precipitation. In this study, seasonal precipitation reconstructions were developed using tree-ring chronologies from spruce species [...] Read more.

Water availability is a major constraint on socioeconomic development in northeastern Mexico, highlighting the need for effective water resource planning that accounts for the variability and extremes of precipitation. In this study, seasonal precipitation reconstructions were developed using tree-ring chronologies from spruce species (Picea spp.). A representative chronology for Picea mexicana Martínez was developed from two populations and spans the period 1786–2020, while a chronology for Picea martinezii T.F. Patterson was established from three populations covering 1746–2020. Both species exhibited significant positive correlations with January–May precipitation (r = 0.65 and 0.71, respectively; p < 0.01) and negative correlations with maximum temperature over the same period (r = −0.52 and −0.59, respectively). Two January–May precipitation reconstructions were produced for periods with adequate sample depth (EPS > 0.85): 1851–2020 for P. mexicana and 1821–2020 for P. martinezii. Both reconstructions revealed pronounced interannual variability, with recurrent droughts and persistently dry conditions, particularly evident in the P. mexicana series. Spatial correlation analyses indicated a historical link between reconstructed precipitation and the El Niño–Southern Oscillation (ENSO). These results highlight the value of spruce species for dendroclimatic reconstruction and their sensitivity to precipitation variability, especially as rising maximum temperatures may compromise their persistence in the Sierra Madre Oriental. Full article

(This article belongs to the Special Issue Forest Ecosystems in a Changing Climate)

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35 pages, 10456 KiB

Open AccessArticle

Amplified Westward SAPS Flows near Magnetic Midnight in the Vicinity of the Harang Region

by Ildiko Horvath and Brian C. Lovell

Atmosphere 2025, 16(7), 862; https://doi.org/10.3390/atmos16070862 (registering DOI) - 15 Jul 2025

Abstract

Rare (only 10) observations, made in the southern topside ionosphere during 2015–2016, demonstrate the amplification of westward subauroral polarization streams (SAPS) up to 3000 m/s near the Harang region. The observed amplified SAPS flows were streaming antisunward after midnight and sunward at midnight, [...] Read more.

Rare (only 10) observations, made in the southern topside ionosphere during 2015–2016, demonstrate the amplification of westward subauroral polarization streams (SAPS) up to 3000 m/s near the Harang region. The observed amplified SAPS flows were streaming antisunward after midnight and sunward at midnight, where the dusk convection cell intruded dawnward. One SAPS event illustrates the elevated electron temperature (Te; ~5500 K) and the stable auroral red arc developed over Rothera. Three inner-magnetosphere SAPS events depict the Harang region’s earthward edge within the plasmasheet’s earthward edge, where the outward SAPS electric (E) field (within the downward Region 2 currents) and inward convection E field (within the upward Region 2 currents) converged. Under isotropic or weak anisotropic conditions, the hot zone was fueled by the interaction of auroral kilometric radiation waves and electron diamagnetic currents. Generated for the conjugate topside ionosphere, the SAMI3 simulations reproduced the westward SAPS flow in the deep electron density trough, where Te became elevated, and the dawnward-intruding westward convection flows. We conclude that the near-midnight westward SAPS flow became amplified because of the favorable conditions created near the Harang region by the convection E field reaching subauroral latitudes and the positive feedback mechanisms in the SAPS channel. Full article

(This article belongs to the Special Issue Feature Papers in Upper Atmosphere (2nd Edition))

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21 pages, 15127 KiB

Open AccessArticle

Assessing the Influences of Leaf Functional Traits on Plant Performances Under Dust Deposition and Microplastic Retention

by Mamun Mandal, Anamika Roy, Shubhankar Ghosh, Achinta Mondal, Arkadiusz Przybysz, Robert Popek, Totan Ghosh, Sandeep Kumar Dash, Ganesh Kumar Agrawal, Randeep Rakwal and Abhijit Sarkar

Atmosphere 2025, 16(7), 861; https://doi.org/10.3390/atmos16070861 (registering DOI) - 15 Jul 2025

Abstract

Since airborne microplastics (AMPs) are a recent and unexplored field of study, there are several unresolved issues regarding their effects on plants. The accumulating potential of AMPs and their effect on the biochemical parameters of ten different plant species in an Indian city [...] Read more.

Since airborne microplastics (AMPs) are a recent and unexplored field of study, there are several unresolved issues regarding their effects on plants. The accumulating potential of AMPs and their effect on the biochemical parameters of ten different plant species in an Indian city environment were assessed. The four types of AMPs deposited in the phyllosphere—fragment (30.76%), film (28.95%), fiber (22.61%), and pellet (17.68%)—were examined using stereomicroscopy and fluorescence microscopy. The air pollution tolerance index (APTI) was determined, and other biochemical parameters such as proline, phenol, malondialdehyde, carotenoids, superoxide dismutase, catalase, and peroxidase were also measured. The findings showed that in the case of polymers type, PE (30%) was more abundant than others, followed by PET (17%), PP (15%), PVC (13%), PVA (10%), PS (7%), ABS (5%), and PMMA (3%). Clerodendrum infortunatum L., Calotropis procera (Aiton) W.T. Aiton, and Mangifera indica L. all showed a strong APTI and also exhibited significantly higher amounts of AMP accumulation. Principal component analysis showed a stronger association between phyllospheric AMPs and biochemical parameters. Additionally, the correlation analysis revealed that the presence of accumulated AMPs may significantly influence the biochemical parameters of the plants. Thus, it can be concluded that the different plant species are uniquely specialized in AMP accumulation, which is significantly impacted by the plants’ APTI as well as other biochemical parameters. Full article

(This article belongs to the Section Aerosols)

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23 pages, 8407 KiB

Open AccessArticle

Assessing the Combined Influence of Indoor Air Quality and Visitor Flow Toward Preventive Conservation at the Peggy Guggenheim Collection

by Maria Catrambone, Emiliano Cristiani, Cristiano Riminesi, Elia Onofri and Luciano Pensabene Buemi

Atmosphere 2025, 16(7), 860; https://doi.org/10.3390/atmos16070860 (registering DOI) - 15 Jul 2025

Abstract

The study at the Peggy Guggenheim Collection in Venice highlights critical interactions between indoor air quality, visitor dynamics, and microclimatic conditions, offering insights into preventive conservation of modern artworks. By analyzing pollutants such as ammonia, formaldehyde, and organic acids, alongside visitor density and [...] Read more.

The study at the Peggy Guggenheim Collection in Venice highlights critical interactions between indoor air quality, visitor dynamics, and microclimatic conditions, offering insights into preventive conservation of modern artworks. By analyzing pollutants such as ammonia, formaldehyde, and organic acids, alongside visitor density and environmental data, the research identified key patterns and risks. Through three seasonal monitoring campaigns, the concentrations of SO₂ (sulphur dioxide), NO (nitric oxide), NO₂ (nitrogen dioxide), NO_x (nitrogen oxides), HONO (nitrous acid), HNO₃ (nitric acid), O₃ (ozone), NH₃ (ammonia), CH₃COOH (acetic acid), HCOOH (formic acid), and HCHO (formaldehyde) were determined using passive samplers, as well as temperature and relative humidity data loggers. In addition, two specific short-term monitoring campaigns focused on NH₃ were performed to evaluate the influence of visitor presence on indoor concentrations of the above compounds and environmental parameters. NH₃ and HCHO concentrations spiked during high visitor occupancy, with NH₃ levels doubling in crowded periods. Short-term NH₃ campaigns confirmed a direct correlation between visitor numbers and the above indoor concentrations, likely due to human emissions (e.g., sweat, breath) and off-gassing from materials. The indoor/outdoor ratios indicated that several pollutants originated from indoor sources, with ammonia and acetic acid showing the highest indoor concentrations. By measuring the number of visitors and microclimate parameters (temperature and humidity) every 3 s, we were able to precisely estimate the causality and the temporal shift between these quantities, both at small time scale (a few minute delay between peaks) and at medium time scale (daily average conditions due to the continuous inflow and outflow of visitors). Full article

(This article belongs to the Section Air Quality)

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17 pages, 2550 KiB

Open AccessArticle

Solar and Wind 24 H Sequenced Prediction Using L-Transform Component and Deep LSTM Learning in Representation of Spatial Pattern Correlation

by Ladislav Zjavka

Atmosphere 2025, 16(7), 859; https://doi.org/10.3390/atmos16070859 (registering DOI) - 15 Jul 2025

Abstract

Spatiotemporal correlations between meteo-inputs and wind–solar outputs in an optimal regional scale are crucial for developing robust models, reliable in mid-term prediction time horizons. Modelling border conditions is vital for early recognition of progress in chaotic atmospheric processes at the destination of interest. [...] Read more.

Spatiotemporal correlations between meteo-inputs and wind–solar outputs in an optimal regional scale are crucial for developing robust models, reliable in mid-term prediction time horizons. Modelling border conditions is vital for early recognition of progress in chaotic atmospheric processes at the destination of interest. This approach is used in differential and deep learning; artificial intelligence (AI) techniques allow for reliable pattern representation in long-term uncertainty and regional irregularities. The proposed day-by-day estimation of the RE production potential is based on first data processing in detecting modelling initialisation times from historical databases, considering correlation distance. Optimal data sampling is crucial for AI training in statistically based predictive modelling. Differential learning (DfL) is a recently developed and biologically inspired strategy that combines numerical derivative solutions with neurocomputing. This hybrid approach is based on the optimal determination of partial differential equations (PDEs) composed at the nodes of gradually expanded binomial trees. It allows for modelling of highly uncertain weather-related physical systems using unstable RE. The main objective is to improve its self-evolution and the resulting computation in prediction time. Representing relevant patterns by their similarity factors in input–output resampling reduces ambiguity in RE forecasting. Node-by-node feature selection and dynamical PDE representation of DfL are evaluated along with long-short-term memory (LSTM) recurrent processing of deep learning (DL), capturing complex spatio-temporal patterns. Parametric C++ executable software with one-month spatial metadata records is available to compare additional modelling strategies. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Atmospheric Sciences)

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16 pages, 19466 KiB

Open AccessArticle

Photochemical Ozone Production Along Flight Trajectories in the Upper Troposphere and Lower Stratosphere and Route Optimisation

by Allan W. Foster, Richard G. Derwent, M. Anwar H. Khan, Dudley E. Shallcross, Mark H. Lowenberg and Rukshan Navaratne

Atmosphere 2025, 16(7), 858; https://doi.org/10.3390/atmos16070858 (registering DOI) - 14 Jul 2025

Abstract

Aviation is widely recognised to have global-scale climate impacts through the formation of ozone (O₃) in the upper troposphere and lower stratosphere (UTLS), driven by emissions of nitrogen oxides (NO_X). Ozone is known to be one of the most [...] Read more.

Aviation is widely recognised to have global-scale climate impacts through the formation of ozone (O₃) in the upper troposphere and lower stratosphere (UTLS), driven by emissions of nitrogen oxides (NO_X). Ozone is known to be one of the most potent greenhouse gases formed from the interaction of aircraft emission plumes with atmospheric species. This paper follows up on previous research, where a Photochemical Trajectory Model was shown to be a robust measure of ozone formation along flight trajectories post-flight. We use a combination of a global Lagrangian chemistry-transport model and a box model to quantify the impacts of aircraft NO_X on UTLS ozone over a five-day timescale. This work expands on the spatial and temporal range, as well as the chemical accuracy reported previously, with a greater range of NO_X chemistry relevant chemical species. Based on these models, route optimisation has been investigated, through the use of network theory and algorithms. This is to show the potential inclusion of an understanding of climate-sensitive regions of the atmosphere on route planning can have on aviation’s impact on Earth’s Thermal Radiation balance with existing resources and technology. Optimised flight trajectories indicated reductions in O₃ formation per unit NO_X are in the range 1–40% depending on the spatial aspect of the flight. Temporally, local winter times and equatorial regions are generally found to have the most significant O₃ formation per unit NO_X; moreover, hotspots were found over the Pacific and Indian Ocean. Full article

(This article belongs to the Section Air Pollution Control)

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25 pages, 6935 KiB

Open AccessArticle

Multi-Scale Analysis of the Mitigation Effect of Green Space Morphology on Urban Heat Islands

by Jie Liu, Xueying Wu, Liyu Pan and Chun-Ming Hsieh

Atmosphere 2025, 16(7), 857; https://doi.org/10.3390/atmos16070857 (registering DOI) - 14 Jul 2025

Abstract

Urban green spaces (UGS) serve as critical mitigators of urban heat islands (UHIs), yet the scale-dependent mechanisms through which UGS morphology regulates thermal effects remain insufficiently understood. This study investigates the multi-scale relationships between UGS spatial patterns and cooling effects in Macao, employing [...] Read more.

Urban green spaces (UGS) serve as critical mitigators of urban heat islands (UHIs), yet the scale-dependent mechanisms through which UGS morphology regulates thermal effects remain insufficiently understood. This study investigates the multi-scale relationships between UGS spatial patterns and cooling effects in Macao, employing morphological spatial pattern analysis (MSPA) to characterize UGS configurations and geographically weighted regression (GWR) to examine city-scale thermal interactions, complemented by patch-scale buffer analyses of area, perimeter, and landscape shape index effects. Results demonstrate that high-UGS-integrity areas significantly enhance cooling capacity (area with proportion of core ≥35% showing optimal performance), while fragmented elements (branches, edges) exacerbate UHIs, with patch-scale analyses revealing nonlinear threshold effects in cooling efficiency. A tripartite classification of UGS by cooling capacity identifies strong mitigation types with optimal shape metrics and cooling extents. These findings establish a tripartite UGS classification system based on cooling performance and identify optimal morphological parameters, advancing understanding of thermal regulation mechanisms in urban environments. This research provides empirical evidence for UGS planning strategies prioritizing core area conservation, morphological optimization, and seasonal adaptation to improve urban climate resilience, offering practical insights for sustainable development in high-density coastal cities. Full article

(This article belongs to the Special Issue Urban Design Guidelines for Climate Change (2nd edition))

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17 pages, 4165 KiB

Open AccessArticle

Assessing the Cooling Effects of Water Bodies Based on Urban Environments: Case Study of Dianchi Lake in Kunming, China

by Zhihao Wang, Ziyang Ma, Yifei Chen, Pengkun Zhu and Lu Wang

Atmosphere 2025, 16(7), 856; https://doi.org/10.3390/atmos16070856 (registering DOI) - 14 Jul 2025

Abstract

This research addresses urban heat island intensification driven by urbanization using Dianchi Lake in Kunming, China, as a case study, aiming to quantitatively evaluate the spatial extent, intensity, and land cover sensitivity differences in the cooling effects of large urban water bodies across [...] Read more.

This research addresses urban heat island intensification driven by urbanization using Dianchi Lake in Kunming, China, as a case study, aiming to quantitatively evaluate the spatial extent, intensity, and land cover sensitivity differences in the cooling effects of large urban water bodies across dry/wet seasons and complex urban landscapes (forest, cropland, and impervious surfaces) to provide a scientific basis for optimizing thermal environments in low-latitude plateau cities. Based on Landsat 8/9 satellite data from dry (January) and wet (May) seasons in 2020 and 2023 used for land surface temperature (LST) retrieval combined with land use data, buffer zone gradient analysis was adopted to quantify the spatial heterogeneity of key cooling indicators within 0–1500 m lakeshore buffers. The results demonstrated significant seasonal differences. The wet season showed a greater cooling extent (600 m) and higher intensity (6.0–6.6 °C) compared with the dry season (400 m; 2.4–3.9 °C). The land cover responses varied substantially, with cropland having the largest influence (600 m), followed by impervious surfaces (400 m), while forest exhibited a minimal effective cooling range (100 m) but localized warming anomalies at 200–400 m. Sensitivity analysis confirmed that impervious surfaces were the most sensitive to water-cooling, followed by cropland, whereas forest showed the lowest sensitivity. Full article

(This article belongs to the Special Issue Urban Heat Islands, Global Warming and Effects)

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26 pages, 6762 KiB

Open AccessArticle

Temporal-Spatial Thermal Comfort Across Urban Blocks with Distinct Morphologies in a Hot Summer and Cold Winter Climate: On-Site Investigations in Beijing

by Tengfei Zhao and Tong Ma

Atmosphere 2025, 16(7), 855; https://doi.org/10.3390/atmos16070855 (registering DOI) - 14 Jul 2025

Abstract

Urban outdoor thermal comfort (OTC) has become an increasingly critical issue under the pressures of urbanization and climate change. Comparative analyses of urban blocks with distinct spatial morphologies are essential for identifying OTC issues and proposing targeted optimization strategies. However, existing studies predominantly [...] Read more.

Urban outdoor thermal comfort (OTC) has become an increasingly critical issue under the pressures of urbanization and climate change. Comparative analyses of urban blocks with distinct spatial morphologies are essential for identifying OTC issues and proposing targeted optimization strategies. However, existing studies predominantly rely on microclimate numerical simulations, while comparative assessments of OTC from the human thermal perception perspective remain limited. This study employs the thermal walk method, integrating microclimatic measurements with thermal perception questionnaires, to conduct on-site OTC investigations across three urban blocks with contrasting spatial morphologies—a business district (BD), a residential area (RA), and a historical neighborhood (HN)—in Beijing, a hot summer and cold winter climate city. The results reveal substantial OTC differences among the blocks. However, these differences demonstrated great seasonal and temporal variations. In summer, BD exhibited the best OTC (mTSV = 1.21), while HN performed the worst (mTSV = 1.72). In contrast, BD showed the poorest OTC in winter (mTSV = −1.57), significantly lower than HN (−1.11) and RA (−1.05). This discrepancy was caused by the unique morphology of different blocks. The sky view factor emerged as a more influential factor affecting OTC over building coverage ratio and building height, particularly in RA (r = 0.689, p < 0.01), but its impact varied by block, season, and sunlight conditions. North–South streets generally perform better OTC than East–West streets, being 0.26 units cooler in summer and 0.20 units warmer in winter on the TSV scale. The study highlights the importance of incorporating more applicable physical parameters to optimize OTC in complex urban contexts and offering theoretical support for designing climate adaptive urban spaces. Full article

(This article belongs to the Section Biometeorology and Bioclimatology)

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14 pages, 1472 KiB

Open AccessArticle

Ionospheric Response to the Extreme Geomagnetic Storm of 10–11 May 2024 Based on Total Electron Content Observations in the Central Asian and East Asian Regions

by Galina Gordiyenko, Feza Arikan, Yuriy Litvinov and Murat Zhiganbaev

Atmosphere 2025, 16(7), 854; https://doi.org/10.3390/atmos16070854 (registering DOI) - 14 Jul 2025

Abstract

The ionospheric response to the major geomagnetic storm (SYM-H = −518 nT) of 10–11 May 2024 was investigated using total electron content (TEC) observations from the Central Asian (CAR) and East Asian (EAR) regions. In the CAR region, shortly after the storm sudden [...] Read more.

The ionospheric response to the major geomagnetic storm (SYM-H = −518 nT) of 10–11 May 2024 was investigated using total electron content (TEC) observations from the Central Asian (CAR) and East Asian (EAR) regions. In the CAR region, shortly after the storm sudden commencement (SC) (17:05 UT on 10 May), during a rapid decrease in SYM-H, a significant TEC decrease (~70%) and a subsequent formation of a prolonged TEC depletion phase on 11 May were observed. The duration of the phase’s maximum intensity seemed to agree with the duration of southward interplanetary magnetic field (IMF) Bz activity. The total duration of the negative phase exceeded 3 days and correlated with the duration of the Auroral Electrojet (AE) index activity. The ionospheric response in the EAR region differed significantly, exhibiting a secondary, deeper TEC decrease (termed “phase 2”) on 12 May, which occurred during a period of reduced AE and IMF Bz activity. The analysis of latitudinal TEC variations in the EAR region revealed that “phase 2” occurred across a geographic latitude range of 31.4° N to 43.9° N (approximately 21° N to 34° N dipole latitude). These results are discussed in the context of potential longitudinal variations in thermospheric composition and meridional circulation during the geomagnetic storm. Full article

(This article belongs to the Special Issue Ionospheric Disturbances and Space Weather)

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3 pages, 133 KiB

Open AccessEditorial

Advancing the Frontiers of Urban Mobile Sources for Air Pollution Prediction and Monitoring: Integrating Deep Learning, Quantum Computing, and Enhanced Sensing

by Zhenyi Xu

Atmosphere 2025, 16(7), 853; https://doi.org/10.3390/atmos16070853 (registering DOI) - 13 Jul 2025

Abstract

The relentless challenge of air pollution, driven by industrialization and urbanization, demands increasingly sophisticated tools for accurate prediction, source attribution, and comprehensive monitoring [...] Full article

(This article belongs to the Special Issue Applying Deep Learning Technology for Spatiotemporal Prediction of Air Pollution from Urban Mobile Sources)

34 pages, 50713 KiB

Open AccessArticle

Air Temperature Extremes in the Mediterranean Region (1940–2024): Synoptic Patterns and Trends

by Georgios Kotsias and Christos J. Lolis

Atmosphere 2025, 16(7), 852; https://doi.org/10.3390/atmos16070852 (registering DOI) - 13 Jul 2025

Abstract

Extreme air temperatures along with the synoptic conditions leading to their appearance are examined for the Mediterranean region for the 85-year period of 1940–2024. The data used are daily (04UTC and 12UTC) grid point (1° × 1°) values of 2 m air temperature, [...] Read more.

Extreme air temperatures along with the synoptic conditions leading to their appearance are examined for the Mediterranean region for the 85-year period of 1940–2024. The data used are daily (04UTC and 12UTC) grid point (1° × 1°) values of 2 m air temperature, 850 hPa air temperature, and 1000 hPa and 500 hPa geopotential heights, obtained from the ERA5 database. For 12UTC and 04UTC, the 2 m air temperature anomalies are calculated and are used for the definition of Extremely High Temperature Days (EHTDs) and Extremely Low Temperature Days (ELTDs), respectively. Overall, 3787 EHTDs and 4872 ELTDs are defined. It is found that EHTDs are evidently more frequent in recent years (increased by 305% since the 1980s) whereas ELTDs are less frequent (decreased by 41% since the 1980s), providing a clear sign of warming of the Mediterranean climate. A multivariate statistical analysis combining factor analysis and k-means clustering, known as spectral clustering, is applied to the data resulting in the definition of nine EHTD and seven ELTD clusters. EHTDs are mainly associated with intense solar heating, blocking anticyclones and warm air advection. ELTDs are connected to intense radiative cooling of the Earth’s surface, cold air advection and Arctic outbreaks. This is a unique study for the Mediterranean region utilizing the high-resolution ERA5 data collected since the 1940s to define and investigate the variability of both high and low temperature extremes using a validated methodology. Full article

(This article belongs to the Section Climatology)

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23 pages, 3967 KiB

Open AccessArticle

Comparative Analysis of Machine Learning Algorithms for Potential Evapotranspiration Estimation Using Limited Data at a High-Altitude Mediterranean Forest

by Stefanos Stefanidis, Konstantinos Ioannou, Nikolaos Proutsos, Ilias Karmiris and Panagiotis Stefanidis

Atmosphere 2025, 16(7), 851; https://doi.org/10.3390/atmos16070851 (registering DOI) - 12 Jul 2025

Abstract

Accurate estimation of potential evapotranspiration (PET) is of paramount importance for water resource management, especially in Mediterranean mountainous environments that are often data-scarce and highly sensitive to climate variability. This study evaluates the performance of four machine learning (ML) regression algorithms—Support Vector Regression [...] Read more.

Accurate estimation of potential evapotranspiration (PET) is of paramount importance for water resource management, especially in Mediterranean mountainous environments that are often data-scarce and highly sensitive to climate variability. This study evaluates the performance of four machine learning (ML) regression algorithms—Support Vector Regression (SVR), Random Forest Regression (RFR), Gradient Boosting Regression (GBR), and K-Nearest Neighbors (KNN)—in predicting daily PET using limited meteorological data from a high-altitude in Central Greece. The ML models were trained and tested using easily available meteorological inputs—temperature, relative humidity, and extraterrestrial solar radiation—on a dataset covering 11 years (2012–2023). Among the tested configurations, RFR showed the best performance (R² = 0.917, RMSE = 0.468 mm/d, MAPE = 0.119 mm/d) when all the above-mentioned input variables were included, closely approximating FAO56–PM outputs. Results bring to light the potential of machine learning models to reliably estimate PET in data-scarce conditions, with RFR outperforming others, whereas the inclusion of the easily estimated extraterrestrial radiation parameter in the ML models training enhances PET prediction accuracy. Full article

(This article belongs to the Special Issue Observation and Modeling of Evapotranspiration)

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19 pages, 3047 KiB

Open AccessArticle

Identifying the Combined Impacts of Sensor Quantity and Location Distribution on Source Inversion Optimization

by Shushuai Mao, Jianlei Lang, Feng Hu, Xiaoqi Wang, Kai Wang, Guiqin Zhang, Feiyong Chen, Tian Chen and Shuiyuan Cheng

Atmosphere 2025, 16(7), 850; https://doi.org/10.3390/atmos16070850 (registering DOI) - 12 Jul 2025

Abstract

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 [...] Read more.

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 belongs to the Section Air Pollution Control)

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13 pages, 3254 KiB

Open AccessArticle

Shifting Climate Patterns in the Brazilian Savanna Evidenced by the Köppen Classification and Drought Indices

by Khályta Willy da Silva Soares, Rafael Battisti, Felipe Puff Dapper, Alexson Pantaleão Machado de Carvalho, Marcos Vinícius da Silva, Jhon Lennon Bezerra da Silva, Henrique Fonseca Elias de Oliveira and Marcio Mesquita

Atmosphere 2025, 16(7), 849; https://doi.org/10.3390/atmos16070849 (registering DOI) - 12 Jul 2025

Abstract

The Brazilian savanna, South America’s second-largest biome, is vital to Brazil’s economy but has suffered from environmental degradation due to unregulated agricultural and urban expansion. This study assesses climate change in the biome from 1961 to 2021 using the Köppen climate classification, drought [...] Read more.

The Brazilian savanna, South America’s second-largest biome, is vital to Brazil’s economy but has suffered from environmental degradation due to unregulated agricultural and urban expansion. This study assesses climate change in the biome from 1961 to 2021 using the Köppen climate classification, drought indices, historical trend analyses, and the climatological water balance. Fourteen municipalities across the biome were analyzed. According to the Köppen classification, most municipalities were identified as Aw (tropical with dry winters) and Am (tropical monsoon), with Dourados, MS, and Sapezal, MT, alternating between Am and Aw. The standardized precipitation index (SPI) revealed changes in rainfall distribution. The Mann–Kendall test detected rising air temperatures in 13 of the 14 municipalities, with Sen’s slope ranging from 0.0156 to 0.0605 °C per year. Rainfall decreased in seven municipalities, with decreases from −4.54 to −12.77 mm per year. The climatological water balance supported the observed decrease in precipitation. The results indicated a clear warming trend and declining rainfall in most of the Brazilian savanna, highlighting potential challenges for water availability in the face of ongoing climate change. Full article

(This article belongs to the Special Issue Climate Change and Agriculture: Impacts and Adaptation (2nd Edition))

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