Atmosphere

21 pages, 4261 KiB

Open AccessArticle

Seasonal Temperature and Precipitation Patterns in Caucasus Landscapes

by Mariam Elizbarashvili, Nazibrola Beglarashvili, Mikheil Pipia, Elizbar Elizbarashvili and Nino Chikhradze

Atmosphere 2025, 16(7), 889; https://doi.org/10.3390/atmos16070889 (registering DOI) - 19 Jul 2025

Abstract

The Caucasus region, characterized by its complex topography and diverse climatic regimes, exhibits pronounced spatial variability in temperature and precipitation patterns. This study investigates the seasonal behavior of air temperature, precipitation, vertical temperature gradients, and inversion phenomena across distinct landscape types using observational [...] Read more.

The Caucasus region, characterized by its complex topography and diverse climatic regimes, exhibits pronounced spatial variability in temperature and precipitation patterns. This study investigates the seasonal behavior of air temperature, precipitation, vertical temperature gradients, and inversion phenomena across distinct landscape types using observational data from 63 meteorological stations for 1950–2022. Temperature trends were analyzed using linear regression, while vertical lapse rates and inversion layers were assessed based on seasonal temperature–elevation relationships. Precipitation regimes were evaluated through Mann-Kendall trend tests and Sen’s slope estimators. Results reveal that temperature regimes are strongly modulated by landscape type and elevation, with higher thermal variability in montane and subalpine zones. Seasonal temperature inversions are most frequent in spring and winter, especially in western lowlands and enclosed valleys. Precipitation patterns vary markedly across landscapes: humid lowlands show autumn–winter maxima, while arid and semi-arid zones peak in spring or late autumn. Some landscapes exhibit secondary maxima and minima, influenced by Mediterranean cyclones and regional atmospheric stability. Statistically significant trends include increasing cool-season precipitation in humid regions and decreasing spring rainfall in arid areas. These findings highlight the critical role of topography and landscape structure in shaping regional climate patterns and provide a foundation for improved climate modeling, ecological planning, and adaptation strategies in the Caucasus. Full article

(This article belongs to the Special Issue Air Temperature and Precipitation and Relationship to Atmospheric Circulation (2nd Edition))

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17 pages, 2076 KiB

Open AccessArticle

Threefold Threshold: Synergistic Air Pollution in Greater Athens Area, Greece

by Aggelos Kladakis, Kyriaki-Maria Fameli, Konstantinos Moustris, Vasiliki D. Assimakopoulos and Panagiotis T. Nastos

Atmosphere 2025, 16(7), 888; https://doi.org/10.3390/atmos16070888 (registering DOI) - 19 Jul 2025

Abstract

This study investigates the health impacts of air pollution in the Greater Athens Area (GAA), Greece, by estimating the Relative Risk (RR) of hospital admissions (HA) for cardiovascular (CVD) and respiratory diseases (RD) from 2018 to 2020. The analysis focuses on daily exceedances [...] Read more.

This study investigates the health impacts of air pollution in the Greater Athens Area (GAA), Greece, by estimating the Relative Risk (RR) of hospital admissions (HA) for cardiovascular (CVD) and respiratory diseases (RD) from 2018 to 2020. The analysis focuses on daily exceedances of key air pollutants—PM₁₀, O₃, and NO₂—based on the “Fair” threshold and above, as defined by the European Union Air Quality Index (EU AQI). Data from ten monitoring stations operated by the Ministry of Environment and Energy were spatially matched with six hospitals across the GAA. A Distributed Lag Non-linear Model (DLNM) was employed to capture both the delayed and non-linear exposure–response (ER) relationships between pollutant exceedances and daily HA. Additionally, the synergistic effects of pollutant interactions were assessed to provide a more comprehensive understanding of cumulative health risks. The combined exposure term showed a peak RR of 1.49 (95% CI: 0.79–2.78), indicating a notable amplification of risk when multiple pollutants exceed thresholds simultaneously. The study utilizes R for data processing and statistical modeling. Findings aim to inform public health strategies by identifying critical exposure thresholds and time-lagged effects, ultimately supporting targeted interventions in urban environments experiencing air quality challenges. Full article

(This article belongs to the Special Issue Urban Air Pollution Exposure and Health Vulnerability)

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24 pages, 50503 KiB

Open AccessArticle

Quantifying the Influence of Sea Surface Temperature Anomalies on the Atmosphere and Precipitation in the Southwestern Atlantic Ocean and Southeastern South America

by Mylene Cabrera, Luciano Pezzi, Marcelo Santini and Celso Mendes

Atmosphere 2025, 16(7), 887; https://doi.org/10.3390/atmos16070887 (registering DOI) - 19 Jul 2025

Abstract

Oceanic mesoscale activity influences the atmosphere in the southwestern and southern sectors of the Atlantic Ocean. However, the influence of high latitudes, specifically sea ice, on mid-latitudes and a better understanding of mesoscale ocean–atmosphere thermodynamic interactions still require further study. To quantify the [...] Read more.

Oceanic mesoscale activity influences the atmosphere in the southwestern and southern sectors of the Atlantic Ocean. However, the influence of high latitudes, specifically sea ice, on mid-latitudes and a better understanding of mesoscale ocean–atmosphere thermodynamic interactions still require further study. To quantify the effects of oceanic mesoscale activity during the periods of maximum and minimum Antarctic sea ice extent (September 2019 and February 2020), numerical experiments were conducted using a coupled regional model and an online two-dimensional spatial filter to remove high-frequency sea surface temperature (SST) oscillations. The largest SST anomalies were observed in the Brazil–Malvinas Confluence and along oceanic fronts in September, with maximum SST anomalies reaching 4.23 °C and −3.71 °C. In February, the anomalies were 2.18 °C and −3.06 °C. The influence of oceanic mesoscale activity was evident in surface atmospheric variables, with larger anomalies also observed in September. This influence led to changes in the vertical structure of the atmosphere, affecting the development of the marine atmospheric boundary layer (MABL) and influencing the free atmosphere above the MABL. Modulations in precipitation patterns were observed, not only in oceanic regions, but also in adjacent continental areas. This research provides a novel perspective on ocean–atmosphere thermodynamic coupling, highlighting the mesoscale role and importance of its representation in the study region. Full article

(This article belongs to the Special Issue Recent Advances in Air-Sea Interactions, Climate Variability, and Predictability (2nd Edition))

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21 pages, 3623 KiB

Open AccessArticle

Stage-Dependent Microphysical Structures of Meiyu Heavy Rainfall in the Yangtze-Huaihe River Valley Revealed by GPM DPR

by Zhongyu Huang, Leilei Kou, Peng Hu, Haiyang Gao, Yanqing Xie and Liguo Zhang

Atmosphere 2025, 16(7), 886; https://doi.org/10.3390/atmos16070886 (registering DOI) - 19 Jul 2025

Abstract

This study presents a comprehensive analysis of the microphysical structures of Meiyu heavy rainfall (near-surface rainfall intensity > 8 mm/h) across different life stages in the Yangtze-Huaihe River Valley (YHRV). We classified the heavy rainfall events into three life stages of developing, mature, [...] Read more.

This study presents a comprehensive analysis of the microphysical structures of Meiyu heavy rainfall (near-surface rainfall intensity > 8 mm/h) across different life stages in the Yangtze-Huaihe River Valley (YHRV). We classified the heavy rainfall events into three life stages of developing, mature, and dissipating using ERA5 reanalysis and IMERG precipitation estimates, and examined vertical microphysical structures using Dual-frequency Precipitation Radar (DPR) data from the Global Precipitation Measurement (GPM) satellite during the Meiyu period from 2014 to 2023. The results showed that convective heavy rainfall during the mature stage exhibits peak radar reflectivity and surface rainfall rates, with the largest near-surface mass weighted diameter (D_m ≈ 1.8 mm) and the smallest droplet concentration (dBN_w ≈ 38). Downdrafts in the dissipating stage preferentially remove large ice particles, whereas sustained moisture influx stabilizes droplet concentrations. Stratiform heavy rainfall, characterized by weak updrafts, displays narrower particle size distributions. During dissipation, particle breakups dominate, reducing D_m while increasing dBN_w. The analysis of the relationship between microphysical parameters and rainfall rate revealed that convective heavy rainfall shows synchronized growth of D_m and dBN_w during the developing stage, with D_m peaking at about 2.1 mm near 70 mm/h before stabilizing in the mature stage, followed by small-particle dominance in the dissipating stage. In contrast, stratiform rainfall exhibits a “small size, high concentration” regime, where the rainfall rate correlates primarily with increasing dBN_w. Additionally, convective heavy rainfall demonstrates about 22% higher precipitation efficiency than stratiform systems, while stratiform rainfall shows a 25% efficiency surge during the dissipation stage compared to other stages. Full article

(This article belongs to the Section Meteorology)

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22 pages, 825 KiB

Open AccessReview

Research on the Emission of Biogenic Volatile Organic Compounds from Terrestrial Vegetation

by Dingyi Pei, Anzhi Wang, Lidu Shen and Jiabing Wu

Atmosphere 2025, 16(7), 885; https://doi.org/10.3390/atmos16070885 (registering DOI) - 19 Jul 2025

Abstract

Biogenic volatile organic compounds (BVOCs) are low-boiling-point compounds commonly synthesized by secondary metabolic pathways in plants. As key precursors of ozone (O₃) and secondary organic aerosols (SOA), BVOCs play a critical role in ecosystem-atmosphere interactions. However, their emission from both marine [...] Read more.

Biogenic volatile organic compounds (BVOCs) are low-boiling-point compounds commonly synthesized by secondary metabolic pathways in plants. As key precursors of ozone (O₃) and secondary organic aerosols (SOA), BVOCs play a critical role in ecosystem-atmosphere interactions. However, their emission from both marine and terrestrial ecosystems, as well as their association with climate and the environment, remain poorly characterized. In light of recent advances in BVOC research, including the establishment of emission inventories, identification of driving factors, and evaluation of ecological and environmental impacts, this study reviews the latest advancements in the field. The findings underscore that the carbon losses via BVOC emission should not be overlooked when estimating the terrestrial carbon balance. Additionally, more work needs to be conducted to quantify the emission factors of specific tree species and to establish links between BVOC emission and climate or environment change. This study contributes to a deeper understanding of vegetation ecology and its environmental functions. Full article

(This article belongs to the Special Issue Atmospheric Particulate Matter: Origin, Sources, and Composition)

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13 pages, 3056 KiB

Open AccessArticle

Analysis of Weather Conditions and Synoptic Systems During Different Stages of Power Grid Icing in Northeastern Yunnan

by Hongwu Wang, Ruidong Zheng, Gang Luo and Guirong Tan

Atmosphere 2025, 16(7), 884; https://doi.org/10.3390/atmos16070884 - 18 Jul 2025

Abstract

Various data such as power grid sensors and manual observed icing, CMA (China Meteorological Administration) Land Surface Data Assimilation System (CLDAS) products, and the Fifth Generation Atmospheric Reanalysis of the Global Climate from Europe Center of Middle Range Weather Forecast (ERA5) are adopted [...] Read more.

Various data such as power grid sensors and manual observed icing, CMA (China Meteorological Administration) Land Surface Data Assimilation System (CLDAS) products, and the Fifth Generation Atmospheric Reanalysis of the Global Climate from Europe Center of Middle Range Weather Forecast (ERA5) are adopted to diagnose an icing process under a cold surge during 16–23 December 2023 in northeastern Yunnan Province. The results show that: (1) in the early stage of the process, mainly the freezing types, such as GG (temperature > 0 °C, relative humidity ≥ 75%) and DG (temperature < 0 °C, relative humidity ≥ 75%), occur. At the end of the process, an increase in icing type as GD (temperature > 0 °C, relative humidity < 75%) appears. (2) Significant differences exist in the elements during different stages of icing, and the atmospheric thermal, dynamic, and water vapor conditions are conducive to the occurrence of freezing rain during ice accretion. The main impact weather systems of this process include a strong high ridge in the mid to high latitudes of East Asia, transverse troughs in front of the high ridge south to Lake Baikal, low altitude troughs, and ground fronts. The transverse trough in front of the high ridge can cause cold air to accumulate and then move eastward and southward. The southerly flows, surface fronts, and other low-pressure systems can provide powerful thermodynamic and moisture conditions for ice accumulation. Full article

(This article belongs to the Section Meteorology)

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19 pages, 13416 KiB

Open AccessArticle

Unveiling PM_2.5 Transport Pathways: A Trajectory-Channel Model Framework for Spatiotemporally Quantitative Source Apportionment

by Yong Pan, Jie Zheng, Fangxin Fang, Fanghui Liang, Mengrong Yang, Lei Tong and Hang Xiao

Atmosphere 2025, 16(7), 883; https://doi.org/10.3390/atmos16070883 - 18 Jul 2025

Abstract

In this study, we introduced a novel Trajectory-Channel Transport Model (TCTM) to unravel spatiotemporal dynamics of PM_2.5 pollution. By integrating high-resolution simulations from the Weather Research and Forecasting (WRF) model with the Nested Air-Quality Prediction Modeling System (WRF-NAQPMS) and 72 h backward-trajectory [...] Read more.

In this study, we introduced a novel Trajectory-Channel Transport Model (TCTM) to unravel spatiotemporal dynamics of PM_2.5 pollution. By integrating high-resolution simulations from the Weather Research and Forecasting (WRF) model with the Nested Air-Quality Prediction Modeling System (WRF-NAQPMS) and 72 h backward-trajectory analysis, TCTM enables the precise identification of source regions, the delineation of key transport corridors, and a quantitative assessment of regional contributions to receptor sites. Focusing on four Yangtze River Delta cities (Hangzhou, Shanghai, Nanjing, Hefei) during a January 2020 pollution event, the results demonstrate that TCTM’s Weighted Concentration Source (WCS) and Source Pollution Characteristic Index (SPCI) outperform traditional PSCF and CWT methods in source-attribution accuracy and resolution. Unlike receptor-based statistical approaches, TCTM reconstructs pollutant transport processes, quantifies spatial decay, and assigns contributions via physically interpretable metrics. This innovative framework offers actionable insights for targeted air-quality management strategies, highlighting its potential as a robust tool for pollution mitigation planning. Full article

(This article belongs to the Special Issue Feature Papers in Atmospheric Techniques, Instruments, and Modeling)

12 pages, 863 KiB

Open AccessArticle

Occurrence and Mitigation of PM_2.5, NO₂, CO and CO₂ in Homes Due to Cooking and Gas Stoves

by Daniel Jaffe, Devon Nirschl and Stephanie Birman

Atmosphere 2025, 16(7), 882; https://doi.org/10.3390/atmos16070882 - 18 Jul 2025

Abstract

We surveyed the air quality conditions in 18 homes with gas stoves for PM_2.5, CO₂, NO₂ and CO using calibrated low-cost sensors. In each home, participants were asked to cook as usual, but to record their cooking activities [...] Read more.

We surveyed the air quality conditions in 18 homes with gas stoves for PM_2.5, CO₂, NO₂ and CO using calibrated low-cost sensors. In each home, participants were asked to cook as usual, but to record their cooking activities and mitigation efforts (windows, ventilation fans, etc.). All homes showed enhanced pollutants during, and immediately after, times of cooking or stove use. For each home, we quantified the minutes per day and minutes per minute of cooking over known health thresholds for each pollutant. On average, homes exhibited 38 min per day over one or more of these thresholds, with PM_2.5 and NO₂ being the pollutants of greatest concern. Six homes had much higher occurrences over the health thresholds, averaging 73 min per day. We found an average of 1.0 min over one or more of the health thresholds per minute of cooking when no mitigation was used, whereas when mitigation was used (filtration or vent fan), this value was reduced by 34%. We further investigated several mitigation methods including natural diffusion, a commercial HEPA filter unit, a commercial O₃ scrubber and a ventilation fan. We found that the HEPA unit was highly effective for PM_2.5 but had no impact on any of the gaseous pollutants. The O₃ scrubber was moderately effective for NO₂ but had little impact on the other pollutants. The ventilation fan was highly effective for all pollutants and reduced the average pollutant lifetime significantly. Under controlled test conditions, the pollutant lifetime (or time to reach 37% of the original concentration), was reduced from an average of 45 min (with no ventilation) to 7 min. While no commercial filter showed efficacy for both PM_2.5 and NO₂, the fact that each could be removed individually suggests that a combined filter for both pollutants could be developed, which would significantly reduce health impacts in homes with gas stoves. Full article

(This article belongs to the Special Issue Indoor Air Pollution: A Silent Threat to Human Health and the Atmosphere)

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16 pages, 1939 KiB

Open AccessArticle

Exploring the Explainability of a Machine Learning Tool to Improve Severe Thunderstorm Wind Reports

by Elizabeth Tirone, William A. Gallus, Jr. and Alexander J. Hamilton

Atmosphere 2025, 16(7), 881; https://doi.org/10.3390/atmos16070881 - 18 Jul 2025

Abstract

Output from a machine learning tool that assigns a probability that a severe thunderstorm wind report was caused by severe intensity wind was evaluated to understand counterintuitive cases where reports that had a high (low) wind speed received a low (high) diagnosed probability. [...] Read more.

Output from a machine learning tool that assigns a probability that a severe thunderstorm wind report was caused by severe intensity wind was evaluated to understand counterintuitive cases where reports that had a high (low) wind speed received a low (high) diagnosed probability. Meteorological data for these cases was compared to that for valid cases where the machine learning probability seemed consistent with the observed severity of the winds. The comparison revealed that the cases with high winds but low probabilities occurred in less conducive environments for severe wind production (less instability, greater low-level relative humidity, weaker lapse rates) than in the cases where high winds occurred with high probabilities. Cases with a low speed but a high probability had environmental characteristics that were more conducive to producing severe wind. These results suggest that the machine learning model is assigning probabilities based on storm modes that more often have measured severe wind speeds (i.e., clusters of cells and bow echoes), and counterintuitive values may reflect events where storm interactions or other smaller-scale features play a bigger role. In addition, some evidence suggests improper reporting may be common for some of these counterintuitive cases. Full article

(This article belongs to the Special Issue Feature Papers in Atmospheric Techniques, Instruments, and Modeling (2nd Edition))

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22 pages, 37656 KiB

Open AccessArticle

Investigating Urban Heat Islands in Miami, Florida, Utilizing Planet and Landsat Satellite Data

by Suraj K C, Anuj Chiluwal, Lalit Pun Magar and Kabita Paudel

Atmosphere 2025, 16(7), 880; https://doi.org/10.3390/atmos16070880 - 18 Jul 2025

Abstract

Miami, Florida, renowned for its cultural richness and coastal beauty, also faces the concerning challenges created by urban heat islands (UHIs). As one of the hottest cities of the United States, Miami is facing escalating temperatures and threatening heat-related vulnerabilities due to urbanization [...] Read more.

Miami, Florida, renowned for its cultural richness and coastal beauty, also faces the concerning challenges created by urban heat islands (UHIs). As one of the hottest cities of the United States, Miami is facing escalating temperatures and threatening heat-related vulnerabilities due to urbanization and climate change. Our study addresses the critical issue of mapping and investigating UHIs in complex urban settings. This study leveraged Planet satellite data and Landsat data to conceptualize and develop appropriate mitigation strategies for UHIs in Miami. Utilizing the Planet satellite imagery and Landsat data, we conducted a combined study of land cover and land surface temperature variations within the city. This approach fuses remotely sensed data to identify the UHI hotspots. This study aims for dynamic approaches for UHI mitigation. This includes studying the status of green spaces present in the city, possible expansion of urban green spaces, the propagation of cool roof initiatives, and exploring the recent climatic trend of the city. The research revealed that built-up areas consistently showed higher land surface temperatures while zones with dense vegetation have lower surface temperatures, supporting the role of urban green spaces in surface temperature reduction. This research can also set a robust model for addressing UHIs in other cities facing rapid urbanization and experiencing mounting temperatures each passing year by helping in assessing LST, land cover, and related spectral indices as well. Full article

(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)

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40 pages, 7235 KiB

Open AccessArticle

New Challenges for Tropical Cyclone Track and Intensity Forecasting in an Unfavorable External Environment in the Western North Pacific—Part II: Intensifications near and North of 20° N

by Russell L. Elsberry, Hsiao-Chung Tsai, Wen-Hsin Huang and Timothy P. Marchok

Atmosphere 2025, 16(7), 879; https://doi.org/10.3390/atmos16070879 - 17 Jul 2025

Abstract

Part I of this two-part documentation of the ECMWF ensemble (ECEPS) new tropical cyclone track and intensity forecasting challenges during the 2024 western North Pacific season described four typhoons that started well to the south of an unfavorable external environment north of 20° [...] Read more.

Part I of this two-part documentation of the ECMWF ensemble (ECEPS) new tropical cyclone track and intensity forecasting challenges during the 2024 western North Pacific season described four typhoons that started well to the south of an unfavorable external environment north of 20° N. In this Part II, five other 2024 season typhoons that formed and intensified near and north of 20° N are documented. One change is that the Cooperative Institute for Meteorological Satellite Studies ADT + AIDT intensities derived from the Himawari-9 satellite were utilized for initialization and validation of the ECEPS intensity forecasts. Our first objective of providing earlier track and intensity forecast guidance than the Joint Typhoon Warning Center (JTWC) five-day forecasts was achieved for all five typhoons, although the track forecast spread was large for the early forecasts. For Marie (06 W) and Ampil (08 W) that formed near 25° N, 140° E in the middle of the unfavorable external environment, the ECEPS intensity forecasts accurately predicted the ADT + AIDT intensities with the exception that the rapid intensification of Ampil over the Kuroshio ocean current was underpredicted. Shanshan (11 W) was a challenging forecast as it intensified to a typhoon while being quasi-stationary near 17° N, 142° E before turning to the north to cross 20° N into the unfavorable external environment. While the ECEPS provided accurate guidance as to the timing and the longitude of the 20° N crossing, the later recurvature near Japan timing was a day early and 4 degrees longitude to the east. The ECEPS provided early, accurate track forecasts of Jebi’s (19 W) threat to mainland Japan. However, the ECEPS was predicting extratropical transition with V_max ~35 kt when the JTWC was interpreting Jebi’s remnants as a tropical cyclone. The ECEPS predicted well the unusual southward track of Krathon (20 W) out of the unfavorable environment to intensify while quasi-stationary near 18.5° N, 125.6° E. However, the rapid intensification as Krathon moved westward along 20° N was underpredicted. Full article

(This article belongs to the Special Issue Typhoon/Hurricane Dynamics and Prediction (2nd Edition))

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17 pages, 1457 KiB

Open AccessArticle

Atmospheric Concentration of Particulate Air Pollutants in the Context of Projected Future Emissions from Motor Vehicles

by Artur Jaworski, Hubert Kuszewski, Krzysztof Balawender and Bożena Babiarz

Atmosphere 2025, 16(7), 878; https://doi.org/10.3390/atmos16070878 - 17 Jul 2025

Abstract

Ambient PM concentrations are influenced by various emission sources and weather conditions such as temperature, wind speed, and direction. Measurements using optical sensors cannot directly link pollution levels to specific sources. Data from roadside monitoring often show that a significant portion of PM [...] Read more.

Ambient PM concentrations are influenced by various emission sources and weather conditions such as temperature, wind speed, and direction. Measurements using optical sensors cannot directly link pollution levels to specific sources. Data from roadside monitoring often show that a significant portion of PM originates from non-traffic sources. Therefore, vehicle-related PM emissions are typically estimated using simulation models based on average emission factors. This study uses the COPERT (Computer Programme to Calculate Emissions from Road Transport) model to estimate emissions from road vehicles under current conditions and future scenarios. These include the introduction of Euro 7 standards and a shift from internal combustion engine (ICE) vehicles to battery electric vehicles (BEVs). The analysis considers exhaust and non-exhaust emissions, as well as indirect emissions from electricity generation for BEV charging. The conducted study showed, among other findings, that replacing internal combustion engine vehicles with electric ones could reduce PM2.5 emissions by approximately 6% (2% when including indirect emissions from electricity generation) and PM10 emissions by about 10% (5% with indirect emissions), compared to the Euro 7 scenario. Full article

(This article belongs to the Section Air Quality)

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23 pages, 3620 KiB

Open AccessArticle

Temperature Prediction at Street Scale During a Heat Wave Using Random Forest

by Panagiotis Gkirmpas, George Tsegas, Denise Boehnke, Christos Vlachokostas and Nicolas Moussiopoulos

Atmosphere 2025, 16(7), 877; https://doi.org/10.3390/atmos16070877 - 17 Jul 2025

Abstract

The rising frequency of heatwaves, combined with the urban heat island effect, increases the population’s exposure to high temperatures, significantly impacting the health of vulnerable groups and the overall well-being of residents. While mesoscale meteorological models can reliably forecast temperatures across urban neighbourhoods, [...] Read more.

The rising frequency of heatwaves, combined with the urban heat island effect, increases the population’s exposure to high temperatures, significantly impacting the health of vulnerable groups and the overall well-being of residents. While mesoscale meteorological models can reliably forecast temperatures across urban neighbourhoods, dense networks of in situ measurements offer more precise data at the street scale. In this work, the Random Forest technique was used to predict street-scale temperatures in the downtown area of Thessaloniki, Greece, during a prolonged heatwave in July 2021. The model was trained using data from a low-cost sensor network, meteorological fields calculated by the mesoscale model MEMO, and micro-environmental spatial features. The results show that, although the MEMO temperature predictions achieve high accuracy during nighttime compared to measurements, they exhibit inconsistent trends across sensor locations during daytime, indicating that the model does not fully account for microclimatic phenomena. Additionally, by using only the observed temperature as the target of the Random Forest model, higher accuracy is achieved, but spatial features are not represented in the predictions. In contrast, the most reliable approach to incorporating spatial characteristics is to use the difference between observed and mesoscale temperatures as the target variable. Full article

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

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18 pages, 1268 KiB

Open AccessReview

Perspectives on the Presence of Environmentally Persistent Free Radicals (EPFRs) in Ambient Particulate Matters and Their Potential Implications for Health Risk

by Senlin Lu, Jiakuan Lu, Xudong Wang, Kai Xiao, Jingying Niuhe, Xinchun Liu and Shinichi Yonemochi

Atmosphere 2025, 16(7), 876; https://doi.org/10.3390/atmos16070876 - 17 Jul 2025

Abstract

Environmental persistent free radicals (EPFRs) represent a class of long-lived, redox-active species with half lives spanning minutes to months. Emerging as critical environmental pollutants, EPFRs pose significant risks due to their persistence, potential for bioaccumulation, and adverse effects on ecosystems and human health. [...] Read more.

Environmental persistent free radicals (EPFRs) represent a class of long-lived, redox-active species with half lives spanning minutes to months. Emerging as critical environmental pollutants, EPFRs pose significant risks due to their persistence, potential for bioaccumulation, and adverse effects on ecosystems and human health. This review critically synthesizes recent advancements in understanding EPFR formation mechanisms, analytical detection methodologies, environmental distribution patterns, and toxicological impacts. While progress has been made in characterization techniques, challenges persist—particularly in overcoming limitations of electron paramagnetic resonance (EPR) spectroscopy and spin-trapping methods in complex environmental matrices. Key knowledge gaps remain, including molecular-level dynamics of EPFR formation, long-term environmental fate under varying geochemical conditions, and quantitative relationships between chronic EPFR exposure and health outcomes. Future research priorities could focus on: (1) atomic-scale mechanistic investigations using advanced computational modeling to resolve formation pathways; (2) development of next-generation detection tools to improve sensitivity and spatial resolution; and (3) integration of EPFR data into region-specific air-quality indices to enhance risk assessment and inform mitigation strategies. Addressing these gaps will advance our capacity to mitigate EPFR persistence and safeguard environmental and public health. Full article

(This article belongs to the Special Issue Composition Analysis and Health Effects of Atmospheric Particulate Matter (2nd Edition))

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17 pages, 1837 KiB

Open AccessArticle

The Impact of Meteorological Variables on Particulate Matter Concentrations

by Amaury de Souza, José Francisco de Oliveira-Júnior, Kelvy Rosalvo Alencar Cardoso, Widinei A. Fernandes and Hamilton Germano Pavao

Atmosphere 2025, 16(7), 875; https://doi.org/10.3390/atmos16070875 - 17 Jul 2025

Abstract

This study assessed the influence of meteorological conditions on particulate matter (PM) concentrations in Campo Grande, Brazil, from May to December 2021. Using statistical analyses, including Pearson’s correlation coefficient and multivariate regression, we analyzed secondary data on PM2.5 and PM10 concentrations and meteorological [...] Read more.

This study assessed the influence of meteorological conditions on particulate matter (PM) concentrations in Campo Grande, Brazil, from May to December 2021. Using statistical analyses, including Pearson’s correlation coefficient and multivariate regression, we analyzed secondary data on PM2.5 and PM10 concentrations and meteorological variables from the Federal University of Mato Grosso do Sul’s Physics Department. Daily PM concentrations complied with Brazil’s National Ambient Air Quality Standards (PQAr). The PM2.5/PM10 ratios averaged 0.436 (hourly) and 0.442 (daily), indicating a mix of fine and coarse particles. Significant positive correlations were found with temperature, while relative humidity showed a negative correlation, reducing PM levels through deposition. Wind speed had no significant impact. Meteorological influences suggest that air quality management should be tailored to regional conditions, particularly addressing local emission sources like vehicular traffic and biomass burning. Full article

(This article belongs to the Special Issue Indoor Air Pollution Monitoring: Multi-Pollutant Exposure and Risk Assessment)

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15 pages, 3070 KiB

Open AccessArticle

Characteristics and Sources of VOCs During a Period of High Ozone Levels in Kunming, China

by Chuantao Huang, Yufei Ling, Yunbo Chen, Lei Tong, Yuan Xue, Chunli Liu, Hang Xiao and Cenyan Huang

Atmosphere 2025, 16(7), 874; https://doi.org/10.3390/atmos16070874 - 17 Jul 2025

Abstract

The increasing levels of ozone pollution have become a significant environmental issue in urban areas worldwide. Previous studies have confirmed that the urban ozone pollution in China is mainly controlled by volatile organic compounds (VOCs) rather than nitrogen oxides. Therefore, a study on [...] Read more.

The increasing levels of ozone pollution have become a significant environmental issue in urban areas worldwide. Previous studies have confirmed that the urban ozone pollution in China is mainly controlled by volatile organic compounds (VOCs) rather than nitrogen oxides. Therefore, a study on the emission characteristics and source analysis of VOCs is important for controlling urban ozone pollution. In this study, hourly concentrations of 57 VOC species in four groups were obtained in April 2022, a period of high ozone pollution in Kunming, China. The ozone formation potential analysis showed that the accumulated reactive VOCs significantly contributed to the subsequent ozone formation, particularly aromatics (44.16%) and alkanes (32.46%). In addition, the ozone production rate in Kunming is mainly controlled by VOCs based on the results of the empirical kinetic modeling approach (K_NOx/K_VOCs = 0.25). The hybrid single-particle Lagrangian integrated trajectory model and polar coordinate diagram showed high VOC and ozone concentrations from the southwest outside the province (50.28%) and the south in local areas (12.78%). Six factors were obtained from the positive matrix factorization model: vehicle exhaust (31.80%), liquefied petroleum gas usage (24.16%), the petrochemical industry (17.81%), fuel evaporation (11.79%), coal burning (7.47%), and solvent usage (6.97%). These findings underscore that reducing anthropogenic VOC emissions and strengthening controls on the related sources could provide a scientifically robust strategy for mitigating ozone pollution in Kunming. Full article

(This article belongs to the Section Air Quality)

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19 pages, 2337 KiB

Open AccessArticle

Gas–Particle Partitioning and Temporal Dynamics of Pesticides in Urban Atmosphere Adjacent to Agriculture

by Dani Khoury, Supansa Chimjarn, Olivier Delhomme and Maurice Millet

Atmosphere 2025, 16(7), 873; https://doi.org/10.3390/atmos16070873 - 17 Jul 2025

Abstract

Air pollution caused by pesticide residues is an emerging concern in urban environments influenced by nearby agricultural activities. In this study, weekly air samples were collected between May 2018 and March 2020 in Strasbourg, France, to quantify 104 pesticides in both gas and [...] Read more.

Air pollution caused by pesticide residues is an emerging concern in urban environments influenced by nearby agricultural activities. In this study, weekly air samples were collected between May 2018 and March 2020 in Strasbourg, France, to quantify 104 pesticides in both gas and particle phases using GC-MS/MS and LC-MS/MS. Herbicides and fungicides were the most frequently detected classes, appearing in 98% of both phases followed by insecticides. Key compounds such as metalaxyl-M, diphenylamine, and bifenthrin were present in over 90% of samples. Concentrations ranged from 2.5 to 63 ng m⁻³ weekly, with cumulative annual loads exceeding 1200 ng m⁻³. Gas–particle partitioning revealed that highly volatile compounds like azinphos-ethyl favored the gas phase, while less volatile ones like bifenthrin and tebuconazole partitioned >95% into particles. A third-degree polynomial regression (R² of 0.74) revealed a nonlinear relationship between Kₚ and particle-phase concentrations, highlighting a threshold above Kₚ of 0.025 beyond which compounds accumulate disproportionately in the particulate phase. Seasonal variability showed that 36% of the annual pesticide load occurred in autumn, with total airborne levels peaking near 400 ng m⁻³, while the lowest load occurred during summer. Principal component analysis identified rainfall and total suspended particles as major drivers of pesticide phase distribution. The inhalation health risk assessed yielded hazard index values < 1 × 10⁻⁷ for all population groups, suggesting negligible non-cancer risk. This study highlights the prevalence, seasonal dynamics, and partition behavior of airborne pesticides in urban air and underscores the need for regulatory attention to this overlooked exposure route. Full article

(This article belongs to the Section Air Quality)

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10 pages, 1640 KiB

Open AccessCommunication

Investigating the Effects of the Solar Eclipse on the Atmosphere over Land and Oceanic Regions: Observations from Ground Stations and COSMIC2 Data

by Ghouse Basha, M. Venkat Ratnam, Jonathan H. Jiang and Kishore Pangaluru

Atmosphere 2025, 16(7), 872; https://doi.org/10.3390/atmos16070872 - 17 Jul 2025

Abstract

The impacts of the solar eclipse that occurred on 8 April 2024 over the United States on various atmospheric parameters are investigated. We analyzed surface and vertical profiles of temperature and humidity to understand how this eclipse affected the atmosphere from the ground [...] Read more.

The impacts of the solar eclipse that occurred on 8 April 2024 over the United States on various atmospheric parameters are investigated. We analyzed surface and vertical profiles of temperature and humidity to understand how this eclipse affected the atmosphere from the ground to the stratosphere. Our findings show a significant response throughout the atmospheric range. The eclipse caused a decrease in shortwave radiation, leading to cooler Earth surfaces and a subsequent drop in surface temperature. This cooling effect also resulted in high relative humidity and lower wind speeds at the surface. Furthermore, GPS radio occultation data from COSMIC-2 revealed a decrease in tropospheric temperature and increase in stratospheric temperature during the eclipse. We also observed a reduction in both the temperature and height of the tropopause. The uniqueness of the present investigations lies in delineating the solar eclipse’s effects on the land and ocean. Our analysis indicates that land regions experienced a more pronounced temperature change compared to ocean regions. Full article

(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)

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13 pages, 2743 KiB

Open AccessCommunication

Evaluating Air Pollution in South African Priority Areas: A Qualitative Comparison of Satellite and In-Situ Data

by Nasiphi Ngcoliso, Lerato Shikwambana, Zintle Mbulawa, Moleboheng Molefe and Mahlatse Kganyago

Atmosphere 2025, 16(7), 871; https://doi.org/10.3390/atmos16070871 - 17 Jul 2025

Abstract

Validating satellite data is essential to ensure its accuracy, reliability, and practical applicability. Such validation underpins scientific research, operational use, and informed policymaking by confirming that space-based measurements reflect real-world conditions. This is typically achieved by comparing satellite observations with ground-based measurements or [...] Read more.

Validating satellite data is essential to ensure its accuracy, reliability, and practical applicability. Such validation underpins scientific research, operational use, and informed policymaking by confirming that space-based measurements reflect real-world conditions. This is typically achieved by comparing satellite observations with ground-based measurements or established reference standards. Without thorough validation, data integrity is compromised, which can negatively affect decisions and economic outcomes. In this study, we validated data from the Sentinel-5P TROPOspheric Monitoring Instrument (TROPOMI) by comparing it with ground-based measurements from the South African Air Quality Information System (SAAQIS). The analysis focused on three monitoring stations—Kliprivier, Lephalale, and Middelburg—over the course of 2022. The pollutants examined include sulfur dioxide (SO₂), nitrogen dioxide (NO₂), and carbon monoxide (CO). The results indicate that CO was the predominant pollutant across all sites, particularly in industrial areas. The study also found that satellite data generally overestimated pollution levels, especially during the winter months, emphasizing the importance of robust ground-based validation. Additionally, data quality challenges such as gaps and temporal misalignments affected the accuracy of both satellite and ground datasets. Lastly, the study shows the discrepancy between the ground-based instruments in South Africa and the TROPOMI, and it suggests how these instruments can be incorporated to provide a better understanding of the air quality. Full article

(This article belongs to the Special Issue Study of Air Pollution Based on Remote Sensing (2nd Edition))

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