Atmosphere

12 pages, 2212 KiB

Open AccessArticle

Vertical Evolution of Volatile Organic Compounds from Unmanned Aerial Vehicle Measurements in the Pearl River Delta, China

by Meng-Xue Tang, Bi-Xuan Wang, Yong Cheng, Hui Zeng and Xiao-Feng Huang

Atmosphere 2025, 16(8), 955; https://doi.org/10.3390/atmos16080955 (registering DOI) - 10 Aug 2025

Abstract

The vertical distribution of volatile organic compounds (VOCs) within the planetary boundary layer (PBL) is critical for understanding ozone (O₃) formation, yet knowledge remains limited in complex urban environments. In this study, vertical measurements of 117 VOC species were conducted using [...] Read more.

The vertical distribution of volatile organic compounds (VOCs) within the planetary boundary layer (PBL) is critical for understanding ozone (O₃) formation, yet knowledge remains limited in complex urban environments. In this study, vertical measurements of 117 VOC species were conducted using an unmanned aerial vehicle (UAV) equipped with a VOC multi-channel sampling system, up to a height of 500 m in Shenzhen, China. Results showed that total VOC (TVOC) concentrations decreased with altitude in the morning, reflecting the influence of surface-level local emissions, but increased with height at midday, likely driven by regional transport and potentially stronger photochemical processes. Source apportionment revealed substantial industrial emissions across all altitudes, vehicular emissions concentrated near the surface, and biomass burning primarily impacting higher layers. Clear evidence of enhanced secondary formation of oxygenated VOCs (OVOCs) was observed along the vertical gradient, particularly at midday, indicating intensified photochemical processes at higher altitudes. These findings underscore the importance of considering vertical heterogeneity in VOC distributions when modeling O₃ formation or developing measures to reduce emissions at different altitudes, and also demonstrate the potential of UAV platforms to provide high-resolution atmospheric chemical data in complex urban environments. Full article

(This article belongs to the Special Issue Biogenic Volatile Organic Compound: Measurement and Emissions)

► Show Figures

Figure 1

19 pages, 60167 KiB

Open AccessArticle

Mapping Ecosystem Carbon Storage in the Nanling Mountains of Guangdong Province Using Machine Learning Based on Multi-Source Remote Sensing

by Wei Wang, Liangbo Tang, Ying Zhang, Junxing Cai, Xiaoyuan Chen and Xiaoyun Mao

Atmosphere 2025, 16(8), 954; https://doi.org/10.3390/atmos16080954 (registering DOI) - 10 Aug 2025

Abstract

Accurate assessment of terrestrial ecosystem carbon storage is essential for understanding the global carbon cycle and informing climate change mitigation strategies. However, traditional estimation models face significant challenges in complex mountainous regions due to difficulties in data acquisition and high ecosystem heterogeneity. This [...] Read more.

Accurate assessment of terrestrial ecosystem carbon storage is essential for understanding the global carbon cycle and informing climate change mitigation strategies. However, traditional estimation models face significant challenges in complex mountainous regions due to difficulties in data acquisition and high ecosystem heterogeneity. This study focuses on the Nanling Mountains in Guangdong Province, China, utilizing the Google Earth Engine (GEE) platform to integrate multi-source remote sensing data (Sentinel-1/2, ALOS, GEDI, MODIS), topographic/climatic variables, and field-collected samples. We employed machine learning models to achieve high-precision prediction and high-resolution mapping of ecosystem carbon storage while also analyzing spatial differentiation patterns. The results indicate that the Random Forest algorithm outperformed Gradient Boosting Decision Tree and Classification and Regression Tree (CART) algorithms by suppressing overfitting through dual randomization. The integration of multi-source data significantly enhanced model performance, achieving a coefficient of determination (R²) of 0.87 for aboveground biomass (AGB) and 0.65 for soil organic carbon (SOC). Integrating precipitation, temperature, and topographic variables improved SOC prediction accuracy by 96.77% compared to using optical data alone. The total carbon storage reached 404 million tons, with forest ecosystems contributing 96.7% of the total and soil carbon pools accounting for 60%. High carbon density zones (>160 Mg C/ha) were mainly concentrated in mid-elevation gentle slopes (300–700 m). The proposed integrated “optical-radar-topography-climate” framework offers a scalable and transferable solution for monitoring carbon storage in complex terrains and provides robust scientific support for carbon sequestration planning in subtropical mountain ecosystems. Full article

(This article belongs to the Topic Big Data Analytics for Climate and Human Impacts on Terrestrial Ecosystems)

► Show Figures

Figure 1

23 pages, 2327 KiB

Open AccessReview

Development and Application of Climate Zoning for Asphalt Pavements in China: A Review and Perspective

by Huanyu Chang, Xuesen Wang and Naren Fang

Atmosphere 2025, 16(8), 953; https://doi.org/10.3390/atmos16080953 (registering DOI) - 10 Aug 2025

Abstract

Asphalt pavements are highly sensitive to climatic conditions, and their performance and longevity are significantly affected by temperature fluctuations, precipitation, and extreme weather events. With increasing climate variability, the development of refined and adaptive climate zoning systems for pavement engineering has become essential. [...] Read more.

Asphalt pavements are highly sensitive to climatic conditions, and their performance and longevity are significantly affected by temperature fluctuations, precipitation, and extreme weather events. With increasing climate variability, the development of refined and adaptive climate zoning systems for pavement engineering has become essential. This study reviews the evolution, methodologies, and applications of asphalt pavement climate zoning in China. First, it delineates the historical progression of climate zoning into three stages, from general natural zoning to the specialized three-indicator model and performance grade (PG) system, and finally to refined spatial processing based on meteorological data. Notably, 48% of provinces have conducted localized zoning studies, with South and Northeast China as key focus areas. Second, this study classifies existing zoning models into three major categories: the traditional three-indicator model (based on high temperature, low temperature, and precipitation), the hydrothermal coefficient model tailored to hot, humid climates, and clustering models incorporating spatial interpolation and multivariate analysis. While the three-indicator model remains the most widely applied due to its simplicity, it may result in coarse divisions in climatically diverse regions. The hydrothermal model offers general guidance but limited accuracy, whereas clustering methods provide high-resolution, adaptive zoning results at the cost of increased computational complexity. Third, the application of climate zoning results to the PG system for asphalt binder classification is analyzed. Although SHRP, LTPP, and C-SHRP formulas are commonly used, C-SHRP tends to overestimate pavement temperatures by 6.0–8.6 °C in China. Approximately 68.8% of studies rely on existing formulas, while 31.2% propose localized conversions to improve PG grading accuracy. Overall, this review identifies both the methodological diversity and key challenges in China’s climate zoning practices and provides a scientific foundation for more performance-oriented, climate-resilient pavement design strategies. Full article

(This article belongs to the Section Climatology)

► Show Figures

Figure 1

26 pages, 2422 KiB

Open AccessArticle

Global Land Monsoon Area Response to Natural Forcing Drivers over the Last Millennium in a Community Earth System Model Ensemble

by Sizheng Gao, Zhiyuan Wang and Jia Jia

Atmosphere 2025, 16(8), 952; https://doi.org/10.3390/atmos16080952 (registering DOI) - 9 Aug 2025

Abstract

The spatial extent of the global land monsoon (GLM), known as the global land monsoon area, is a fundamental climate characteristic with significant socio-ecological implications. While the influence of natural external forcing on GLM intensity during the last millennium (950–1850) is becoming increasingly [...] Read more.

The spatial extent of the global land monsoon (GLM), known as the global land monsoon area, is a fundamental climate characteristic with significant socio-ecological implications. While the influence of natural external forcing on GLM intensity during the last millennium (950–1850) is becoming increasingly understood, the responses of the GLM area remain less explored. This study investigates the forced interdecadal variability in the GLM area using the Community Earth System Model Ensemble, focusing on two key drivers: global mean surface temperature (GMST) changes and variations in the tropical Pacific temperature gradient (TPTG). Our analysis reveals that these drivers explain approximately 33% of forced GLM area variance. Global cooling (Cool-GMST) and weakened Pacific gradients (Weak-TPTG) induce significant area contractions of −0.37% and −0.74%, respectively. Most notably, the response to compound forcing is highly non-linear. Concurrent episodes of strong cooling and Weak-TPTG induce a substantially amplified GLM area reduction of −1.37%, far exceeding the linear sum of the individual driver effects. This non-linear amplification, driven by synergistic decreases in both APR and SPF, challenges the conventional assumptions used to model and attribute monsoon boundary changes. This discovery of a non-linear threshold-dependent behavior in the monsoon’s spatial extent, which contrasts with the more linear response of monsoon intensity, is a key finding of our study. This distinction is critical for interpreting paleoclimate records, and serves as a strong indication that future climate projections must account for such non-linearities to avoid underestimating the risk of abrupt monsoon boundary shifts under combined natural and anthropogenic stressors. Full article

(This article belongs to the Section Climatology)

► Show Figures

Figure 1

22 pages, 7438 KiB

Open AccessArticle

Expanding Continuous Carbon Isotope Measurements of CO₂ and CH₄ in the Italian ICOS Atmospheric Consortium: First Results from the Continental POT Station in Potenza (Basilicata)

by Antonella Buono, Isabella Zaccardo, Francesco D’Amico, Emilio Lapenna, Francesco Cardellicchio, Teresa Laurita, Davide Amodio, Canio Colangelo, Gianluca Di Fiore, Aldo Giunta, Michele Volini, Claudia Roberta Calidonna, Alcide Giorgio di Sarra, Serena Trippetta and Lucia Mona

Atmosphere 2025, 16(8), 951; https://doi.org/10.3390/atmos16080951 - 8 Aug 2025

Abstract

Carbon isotope fractionation is an efficient tool used for the discrimination and differentiation of sinks and emission sources. Carbon dioxide (CO₂) and methane (CH₄) are among the key drivers of climate change, and a detailed evaluation of variations in [...] Read more.

Carbon isotope fractionation is an efficient tool used for the discrimination and differentiation of sinks and emission sources. Carbon dioxide (CO₂) and methane (CH₄) are among the key drivers of climate change, and a detailed evaluation of variations in the ¹³C/¹²C ratio in either compound provides vital information for the field of atmospheric sciences. The Italian atmospheric ICOS (Integrated Carbon Observation System) consortium is currently implementing δ¹³C-CO₂ and δ¹³C-CH₄ measurements, with four observation sites now equipped with Picarro G2201-i CRDS (Cavity Ring-Down Spectrometry) analyzers. In this work, results from the first two months of measurements performed at the Potenza station in southern Italy between 20 February and 20 April 2025 are presented and constitute the first evaluation of continuous atmospheric δ¹³C-CO₂ and δ¹³C-CH₄ measurements from an Italian station. These results provide a first insight on how these measurements can improve the current understanding of CO₂ and CH₄ variability in the Italian peninsula and the central Mediterranean sector. Although preliminary in nature, the findings of these measurements indicate that fossil fuel burning is responsible for the observed peaks in CO₂ concentrations. CH₄ has a generally stable pattern; however, abrupt peaks in its isotopic delta, observed during March, may constitute the first direct evidence in Italy of Saharan dust intrusion affecting carbon isotope fractionation in the atmosphere. This study also introduces an analysis of the weekly behavior in isotopic deltas. Full article

(This article belongs to the Section Air Pollution Control)

► Show Figures

Figure 1

16 pages, 18507 KiB

Open AccessArticle

Spatiotemporal Ionospheric TEC Prediction with Deformable Convolution for Long-Term Spatial Dependencies

by Jie Li, Jian Xiao, Haijun Liu, Xiaofeng Du and Shixiang Liu

Atmosphere 2025, 16(8), 950; https://doi.org/10.3390/atmos16080950 - 7 Aug 2025

Abstract

SA-ConvLSTM is a recently proposed spatiotemporal model for total electron content (TEC) prediction, which effectively catches long-term temporal evolution and global-scale spatial correlations in TEC. However, its reliance on standard convolution limits spatial feature extraction to fixed regular regions, reducing the flexibility for [...] Read more.

SA-ConvLSTM is a recently proposed spatiotemporal model for total electron content (TEC) prediction, which effectively catches long-term temporal evolution and global-scale spatial correlations in TEC. However, its reliance on standard convolution limits spatial feature extraction to fixed regular regions, reducing the flexibility for irregular TEC variations. To address this limitation, we enhance SA-ConvLSTM by incorporating deformable convolution, proposing SA-DConvLSTM. This achieves adaptive spatial feature extraction through learnable offsets in convolutional kernels. Building on this improvement, we design ED-SA-DConvLSTM, a TEC spatiotemporal prediction model based on an encoder–decoder architecture with SA-DConvLSTM as its fundamental block. Firstly, the effectiveness of the model improvement was verified through an ablation experiment. Subsequently, a comprehensive quantitative comparison was conducted between ED-SA-DConvLSTM and baseline models (C1PG, ConvLSTM, and ConvGRU) in the region of 12.5° S–87.5° N and 25° E–180° E. The experimental results showed that the ED-SA-DConvLSTM exhibited superior performance compared to C1PG, ConvGRU, and ConvLSTM, with prediction accuracy improvements of 10.27%, 7.65%, and 7.16% during high solar activity and 11.46%, 4.75%, and 4.06% during low solar activity, respectively. To further evaluate model performance under extreme conditions, we tested the ED-SA-DConvLSTM during four geomagnetic storms. The results showed that the proportion of its superiority over the baseline models exceeded 58%. Full article

(This article belongs to the Section Upper Atmosphere)

► Show Figures

Figure 1

41 pages, 11949 KiB

Open AccessArticle

On the Assessment of Hourly Means of Solar Irradiance at Ground Level in Clear-Sky Conditions by the ERA5, JRA-3Q, and MERRA-2 Reanalyses

by Yves-Marie Saint-Drenan and Lucien Wald

Atmosphere 2025, 16(8), 949; https://doi.org/10.3390/atmos16080949 - 7 Aug 2025

Abstract

Meteorological reanalyses are one of the means to assess the solar irradiance reaching the ground. This paper deals with estimates of the hourly means of irradiance in clear-sky conditions provided by the ERA5, JRA-3Q, and MERRA-2 reanalyses. They are compared to coincident ground-based [...] Read more.

Meteorological reanalyses are one of the means to assess the solar irradiance reaching the ground. This paper deals with estimates of the hourly means of irradiance in clear-sky conditions provided by the ERA5, JRA-3Q, and MERRA-2 reanalyses. They are compared to coincident ground-based measurements from 28 BSRN stations located worldwide, selected by a new algorithm for detecting cloud-free instants. Although ERA5 most often underestimates measurements, it is quite reliable over time because it captures the temporal variability of measurements well and provides a constant level of uncertainty. JRA-3Q offers a complex pattern with negative and positive biases depending on station and season. It captures well the temporal variability but, as a whole, is not reliable over time. None of the three reanalyses is reliable in space. Because of its use of the mean solar time instead of the true solar time, MERRA-2 suffers many drawbacks over intraday scales. Its statistical indicators exhibit marked patterns depending on the season and station. Its assimilation of aerosol properties offers advantages when compared to the climatologies used in ERA5 and JRA-3Q. This work exposes the strengths and weaknesses of each reanalysis in clear-sky conditions and formulates suggestions to providers for further improvements. Full article

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

► Show Figures

Figure 1

17 pages, 3821 KiB

Open AccessArticle

Evaluation Model of Climatic Suitability for Olive Cultivation in Central Longnan, China

by Li Liu, Ying Na and Yun Ma

Atmosphere 2025, 16(8), 948; https://doi.org/10.3390/atmos16080948 - 7 Aug 2025

Abstract

Longnan is the largest olive cultivation area in China. The unique microclimates in Longnan make it an ideal testing ground for climate-resilient cultivation strategies with broader applications across similar regions, yet predictive models linking weather to oil quality remain scarce. This study establishes [...] Read more.

Longnan is the largest olive cultivation area in China. The unique microclimates in Longnan make it an ideal testing ground for climate-resilient cultivation strategies with broader applications across similar regions, yet predictive models linking weather to oil quality remain scarce. This study establishes a climate suitability evaluation model for olive cultivation in central Longnan based on meteorological data and olive quality data in the Fotanggou planting base. Four key climatic factors are identified: cumulative sunshine hours during the fruit coloring to ripening period, average temperature during the fruit coloring to harvesting period, number of cloudy and rainy days during the harvesting period, and relative humidity during the fruit setting to fruit enlargement period. Olive oil quality is graded into three levels (Excellent III, Good II, Fair I) based on acidity, linoleic acid, and peroxide value using K-means clustering. A climate suitability index is developed by integrating these factors, with weights determined via principal component analysis. The model is validated against an olive quality report from the Dabao planting base, showing an 80% match rate. From 1991 to 2023, 87.9% of years exhibit suitable or moderately suitable conditions, with 100% of years in the past decade (2014–2023) reaching “Good” or “Excellent” levels. This model provides a scientific basis for evaluating and predicting olive oil quality, supporting sustainable olive industry development in Longnan. This model provides policymakers and farmers with actionable insights to ensure the long-term sustainability of olive industry amid climate uncertainty. Full article

(This article belongs to the Special Issue Understanding and Forecasting Seasonal Weather and Climate Extreme Events)

► Show Figures

Figure 1

20 pages, 15138 KiB

Open AccessArticle

Optimizing Pedestrian-Friendly Spaces in Xi’an’s Residential Streets: Accounting for PM_2.5 Exposure

by Xina Ma, Handi Xie and Jingwen Wang

Atmosphere 2025, 16(8), 947; https://doi.org/10.3390/atmos16080947 - 7 Aug 2025

Abstract

Urban street canyons in high-density areas exacerbate PM_2.5 accumulation, posing significant public health risks. Through integrated empirical and computational methods—including empirical PM_2.5 and microclimate measurements, multivariate regression analysis, and high-resolution ENVI-met5.1 simulations—this study quantifies the threshold effects of pedestrian-oriented morphological indicators [...] Read more.

Urban street canyons in high-density areas exacerbate PM_2.5 accumulation, posing significant public health risks. Through integrated empirical and computational methods—including empirical PM_2.5 and microclimate measurements, multivariate regression analysis, and high-resolution ENVI-met5.1 simulations—this study quantifies the threshold effects of pedestrian-oriented morphological indicators on PM_2.5 exposure in east–west-oriented residential streets. Key findings include the following: (1) the height-to-width ratio (H/W) negatively correlates with exposure, where H/W = 2.0 reduces the peak concentrations by 37–41% relative to H/W = 0.5 through enhanced vertical advection; (2) the Build-To-Line ratio (BTR) exhibits a positive correlation with exposure, with BTR = 63.2% mitigating exposure by 12–15% compared to BTR = 76.8% by reducing aerodynamic stagnation; (3) pollution exposure can be mitigated by enhancing airflow ventilation within street canyons through architectural facade design. These evidence-based morphological thresholds (H/W ≥ 1.5, BTR ≤ 70%) provide actionable strategies for reducing health risks in polluted urban corridors, supporting China to meet its national air quality improvement targets. Full article

(This article belongs to the Special Issue Characteristics and Control of Particulate Matter)

► Show Figures

Figure 1

18 pages, 4029 KiB

Open AccessArticle

Characterizing CO₂ Emission from Various PHEVs Under Charge-Depleting Conditions

by Nan Yang, Xuetong Lian, Zhenxiao Bai, Liangwu Rao, Junxin Jiang, Jiaqiang Li, Jiguang Wang and Xin Wang

Atmosphere 2025, 16(8), 946; https://doi.org/10.3390/atmos16080946 - 7 Aug 2025

Abstract

With the significant growth in the number of PHEVs, conducting in-depth research on their CO₂ emission characteristics is essential. This study used the Horiba OBS-ONE Portable Emission Measurement System (PEMS) to measure the CO₂ emissions of three Plug-in Hybrid Electric Vehicle [...] Read more.

With the significant growth in the number of PHEVs, conducting in-depth research on their CO₂ emission characteristics is essential. This study used the Horiba OBS-ONE Portable Emission Measurement System (PEMS) to measure the CO₂ emissions of three Plug-in Hybrid Electric Vehicle (PHEV) types: one Series Hybrid Electric Vehicle (S-HEV), one Parallel Hybrid Electric Vehicle (P-HEV), and one Series-Parallel Hybrid Electric Vehicle (SP-HEV), during real driving conditions. The findings show a correlation between acceleration and increased CO₂ emissions for P-HEV, while acceleration has a relatively minor impact on S-HEV and SP-HEV emissions. Under urban driving conditions, the SP-HEV displays the lowest average CO₂ emission rate. However, under suburban and highway conditions, the average CO₂ emission rates follow the order S-HEV > SP-HEV > P-HEV. An analysis of CO₂ emission factors across different road types and vehicle-specific power (VSP) ranges indicates that within low VSP intervals (VSP ≤ 0 for urban, VSP ≤ 5 for suburban, and VSP ≤ 15 for highway roads), the P-HEV exhibits the best CO₂ emission control. As VSP increases, the P-HEV’s emission factors rise under all three road conditions, with its emission control capability weakening when VSP exceeds 5 in urban, 15 in suburban, and 20 on highway roads. For the SP-HEV, CO₂ emission factors increase with VSP in urban and suburban areas but remain stable on highways. The S-HEV shows minimal changes in emission factors with varying VSP. This research provides valuable insights into the CO₂ emission patterns of PHEVs, aiding vehicle optimization and policy development. Full article

(This article belongs to the Special Issue Traffic Related Emission (3rd Edition))

► Show Figures

Figure 1

23 pages, 11564 KiB

Open AccessArticle

Cloud-Based Assessment of Flash Flood Susceptibility, Peak Runoff, and Peak Discharge on a National Scale with Google Earth Engine (GEE)

by Ivica Milevski, Bojana Aleksova, Aleksandar Valjarević and Pece Gorsevski

Atmosphere 2025, 16(8), 945; https://doi.org/10.3390/atmos16080945 - 7 Aug 2025

Abstract

Flash floods, exacerbated by climate change and land use alterations, are among the most destructive natural hazards globally, leading to significant damage and loss of life. In this context, the Flash Flood Potential Index (FFPI), which is a terrain and land surface-based model, [...] Read more.

Flash floods, exacerbated by climate change and land use alterations, are among the most destructive natural hazards globally, leading to significant damage and loss of life. In this context, the Flash Flood Potential Index (FFPI), which is a terrain and land surface-based model, and Google Earth Engine (GEE) were used to assess flood-prone zones across North Macedonia’s watersheds. The presented GEE-based assessment was accomplished by a custom script that automates the FFPI calculation process by integrating key factors derived from publicly available sources. These factors, which define susceptibility to torrential floods, include slope (Copernicus GLO-30 DEM), land cover (Copernicus GLO-30 DEM), soil type (SoilGrids), vegetation (ESA World Cover), and erodibility (CHIRPS). The spatial distribution of average FFPI values across 1396 small catchments (10–100 km²) revealed that a total of 45.4% of the area exhibited high to very high susceptibility, with notable spatial variability. The CHIRPS rainfall data (2000–2024) that combines satellite imagery and in situ measurements was used to estimate peak 24 h runoff and discharge. To improve the accuracy of CHIRPS, the data were adjusted by 30–50% to align with meteorological station records, along with normalized FFPI values as runoff coefficients. Validation against 328 historical river flood and flash flood records confirmed that 73.2% of events aligned with moderate to very high flash flood susceptibility catchments, underscoring the model’s reliability. Thus, the presented cloud-based scenario highlights the potential of the GEE’s efficacy in scalability and robustness for flash flood modeling and regional risk management at national scale. Full article

(This article belongs to the Section Biosphere/Hydrosphere/Land–Atmosphere Interactions)

► Show Figures

Figure 1

16 pages, 1018 KiB

Open AccessArticle

A Study on the Improvement Pathways of Carbon Emission Efficiency in China from a Configurational Perspective Based on the Dynamic Qualitative Comparative Analysis

by Tingyu Tao and Hao Zhang

Atmosphere 2025, 16(8), 944; https://doi.org/10.3390/atmos16080944 - 6 Aug 2025

Abstract

Improving carbon emission efficiency (CEE) is crucial for coordinating economic development and reducing carbon emissions. Drawing on panel data for 30 provinces in China from 2013 to 2022, this paper selects six key antecedent conditions guided by the Technology–Organization–Environment (TOE) framework. Then the [...] Read more.

Improving carbon emission efficiency (CEE) is crucial for coordinating economic development and reducing carbon emissions. Drawing on panel data for 30 provinces in China from 2013 to 2022, this paper selects six key antecedent conditions guided by the Technology–Organization–Environment (TOE) framework. Then the dynamic qualitative comparative analysis (DQCA) is employed to explore CEE improvement pathways from a configurational perspective, and regression analysis is used to compare the driving effects of different pathways. The findings reveal that (1) single factors cannot independently achieve high CEE; instead, multiple factors must work synergistically to form various improvement pathways, including “technology–organization dual-driven”, “environment-dominated”, and “multi-equilibrium” pathways, with industrial structure upgrading as the core factor for improving CEE; (2) temporally, these improvement pathways demonstrate universality, while, spatially, they exhibit significant provincial heterogeneity; and (3) in terms of marginal effects, the “multi-equilibrium” pathway has the strongest driving effect on CEE. The findings provide valuable policy implications for developing targeted CEE enhancement strategies across provinces at different developmental stages. Full article

(This article belongs to the Topic Climate, Health and Cities: Building Aspects for a Resilient Future)

► Show Figures

Figure 1

12 pages, 1678 KiB

Open AccessArticle

Fine-Scale Spatial Distribution of Indoor Radon and Identification of Potential Ingress Pathways

by Dobromir Pressyanov and Dimitar Dimitrov

Atmosphere 2025, 16(8), 943; https://doi.org/10.3390/atmos16080943 - 6 Aug 2025

Abstract

A new generation of compact radon detectors with high sensitivity and fine spatial resolution (1–2 cm scale) was used to investigate indoor radon distribution and identify potential entry pathways. Solid-state nuclear track detectors (Kodak-Pathe LR-115 type II, Dosirad, France), combined with activated carbon [...] Read more.

A new generation of compact radon detectors with high sensitivity and fine spatial resolution (1–2 cm scale) was used to investigate indoor radon distribution and identify potential entry pathways. Solid-state nuclear track detectors (Kodak-Pathe LR-115 type II, Dosirad, France), combined with activated carbon fabric (ACC-5092-10), enabled sensitive, spatially resolved radon measurements. Two case studies were conducted: Case 1 involves a room with elevated radon levels suspected to originate from the floor. Case 2 involves a house with persistently high indoor radon concentrations despite active basement ventilation. In the first case, radon emission from the floor was found to be highly inhomogeneous, with concentrations varying by more than a factor of four. In the second, unexpectedly high radon levels were detected at electrical switches and outlets on walls in the living space, suggesting radon transport through wall voids and entry via non-hermetic electrical fittings. These novel detectors facilitate fine-scale mapping of indoor radon concentrations, revealing ingress routes that were previously undetectable. Their use can significantly enhance radon diagnostics and support the development of more effective mitigation strategies. Full article

(This article belongs to the Special Issue Atmospheric Radon Concentration Monitoring and Measurements (2nd Edition))

► Show Figures

Figure 1

19 pages, 14381 KiB

Open AccessArticle

Temperature and Humidity Anomalies During the Summer Drought of 2022 over the Yangtze River Basin

by Dengao Li, Er Lu, Dian Yuan and Ruisi Liu

Atmosphere 2025, 16(8), 942; https://doi.org/10.3390/atmos16080942 - 6 Aug 2025

Abstract

In the summer of 2022, central and eastern China experienced prolonged extreme high temperatures and severe drought, leading to significant economic losses. To gain a more profound understanding of this drought event and furnish a reference for forecasting similar events in the future, [...] Read more.

In the summer of 2022, central and eastern China experienced prolonged extreme high temperatures and severe drought, leading to significant economic losses. To gain a more profound understanding of this drought event and furnish a reference for forecasting similar events in the future, this study examines the circulation anomalies associated with the drought. Employing a diagnostic method focused on temperature and moisture anomalies, this study introduces a novel approach to quantify and compare the relative significance of moisture transport and warm air dynamics in contributing to the drought. This study examines the atmospheric circulation anomalies linked to the drought event and compares the relative contributions of water vapor transport and warm air activity in causing the drought, using two parameters defined in the paper. The results show the following: (1) The West Pacific Subtropical High (WPSH) was more intense than usual and extended westward, consistently controlling the Yangtze River Basin. Simultaneously, the polar vortex area was smaller and weaker, the South Asian High area was larger and stronger, and it shifted eastward. These factors collectively led to weakened water vapor transport conditions and prevailing subsiding air motions in the Yangtze River Basin, causing frequent high temperatures. (2) By defining I_q and I_t to represent the contributions of moisture and temperature to precipitation, we found that the drought event in the Yangtze River Basin was driven by both reduced moisture supplies in the lower troposphere and higher-than-normal temperatures, with temperature playing a dominant role. Full article

(This article belongs to the Section Meteorology)

► Show Figures

Figure 1

23 pages, 3831 KiB

Open AccessArticle

Estimating Planetary Boundary Layer Height over Central Amazonia Using Random Forest

by Paulo Renato P. Silva, Rayonil G. Carneiro, Alison O. Moraes, Cleo Quaresma Dias-Junior and Gilberto Fisch

Atmosphere 2025, 16(8), 941; https://doi.org/10.3390/atmos16080941 - 5 Aug 2025

Abstract

This study investigates the use of a Random Forest (RF), an artificial intelligence (AI) model, to estimate the planetary boundary layer height (PBLH) over Central Amazonia from climatic elements data collected during the GoAmazon experiment, held in 2014 and 2015, as it is [...] Read more.

This study investigates the use of a Random Forest (RF), an artificial intelligence (AI) model, to estimate the planetary boundary layer height (PBLH) over Central Amazonia from climatic elements data collected during the GoAmazon experiment, held in 2014 and 2015, as it is a key metric for air quality, weather forecasting, and climate modeling. The novelty of this study lies in estimating PBLH using only surface-based meteorological observations. This approach is validated against remote sensing measurements (e.g., LIDAR, ceilometer, and wind profilers), which are seldom available in the Amazon region. The dataset includes various meteorological features, though substantial missing data for the latent heat flux (LE) and net radiation (Rn) measurements posed challenges. We addressed these gaps through different data-cleaning strategies, such as feature exclusion, row removal, and imputation techniques, assessing their impact on model performance using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and

r^{2}

metrics. The best-performing strategy achieved an RMSE of 375.9 m. In addition to the RF model, we benchmarked its performance against Linear Regression, Support Vector Regression, LightGBM, XGBoost, and a Deep Neural Network. While all models showed moderate correlation with observed PBLH, the RF model outperformed all others with statistically significant differences confirmed by paired t-tests. SHAP (SHapley Additive exPlanations) values were used to enhance model interpretability, revealing hour of the day, air temperature, and relative humidity as the most influential predictors for PBLH, underscoring their critical role in atmospheric dynamics in Central Amazonia. Despite these optimizations, the model underestimates the PBLH values—by an average of 197 m, particularly in the spring and early summer austral seasons when atmospheric conditions are more variable. These findings emphasize the importance of robust data preprocessing and higtextight the potential of ML models for improving PBLH estimation in data-scarce tropical environments. Full article

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

► Show Figures

Figure 1

22 pages, 14608 KiB

Open AccessArticle

Temporal and Spatial Evolution of Gross Primary Productivity of Vegetation and Its Driving Factors on the Qinghai-Tibet Plateau Based on Geographical Detectors

by Liang Zhang, Cunlin Xin and Meiping Sun

Atmosphere 2025, 16(8), 940; https://doi.org/10.3390/atmos16080940 - 5 Aug 2025

Abstract

To investigate the spatiotemporal evolution characteristics and primary driving factors of Gross Primary Productivity (GPP) on the Qinghai-Tibet Plateau, we employed an enhanced MODIS-PSN model. Utilizing the fifth-generation global climate reanalysis dataset (ECMWF ERA5), we generated GPP remote sensing products by integrating six [...] Read more.

To investigate the spatiotemporal evolution characteristics and primary driving factors of Gross Primary Productivity (GPP) on the Qinghai-Tibet Plateau, we employed an enhanced MODIS-PSN model. Utilizing the fifth-generation global climate reanalysis dataset (ECMWF ERA5), we generated GPP remote sensing products by integrating six natural factors. Through correlation analysis and geographical detector modeling, we quantitatively analyzed the spatiotemporal dynamics and key drivers of vegetation GPP across the Qinghai-Tibet Plateau from 2001 to 2022. The results demonstrate that GPP changes across the Qinghai-Tibet Plateau display pronounced spatial heterogeneity. The humid northeastern and southeastern regions exhibit significantly positive change rates, primarily distributed across wetland and forest ecosystems, with a maximum mean annual change rate of 12.40 gC/m²/year. In contrast, the central and southern regions display a decreasing trend, with the minimum change rate reaching −1.61 gC/m²/year, predominantly concentrated in alpine grasslands and desert areas. Vegetation GPP on the Qinghai-Tibet Plateau shows significant correlations with temperature, vapor pressure deficit (VPD), evapotranspiration (ET), leaf area index (LAI), precipitation, and radiation. Among the factors analyzed, LAI demonstrates the strongest explanatory power for spatial variations in vegetation GPP across the Qinghai-Tibet Plateau. The dominant factors influencing vegetation GPP on the Qinghai-Tibet Plateau are LAI, ET, and precipitation. The pairwise interactions between these factors exhibit linear enhancement effects, demonstrating synergistic multifactor interactions. This study systematically analyzed the response mechanisms and variations of vegetation GPP to multiple driving factors across the Qinghai-Tibet Plateau from a spatial heterogeneity perspective. The findings provide both a critical theoretical framework and practical insights for better understanding ecosystem response dynamics and drought conditions on the plateau. Full article

(This article belongs to the Special Issue Climate Change Over the Tibetan Plateau and Surroundings on Decadal to Sub-Orbital Timescales and Its Driving Mechanisms)

► Show Figures

Figure 1

19 pages, 3596 KiB

Open AccessArticle

Radon Exposure to the General Population of the Fernald Community Cohort

by John F. Reichard, Swade Barned, Angelico Mendy and Susan M. Pinney

Atmosphere 2025, 16(8), 939; https://doi.org/10.3390/atmos16080939 - 5 Aug 2025

Abstract

The Fernald Feed Materials Production Center (FMPC), located in Fernald, Ohio, USA, released radon (Rn) as a byproduct of the processing of uranium materials during the years from 1951 to 1989. Rn is a colorless, odorless gas that emits charged alpha radiation that [...] Read more.

The Fernald Feed Materials Production Center (FMPC), located in Fernald, Ohio, USA, released radon (Rn) as a byproduct of the processing of uranium materials during the years from 1951 to 1989. Rn is a colorless, odorless gas that emits charged alpha radiation that interacts with cells in the lung and trachea-bronchial tree, leading to DNA damage, mutations, and tumor initiation. The purpose of this project was to use evidence collected by the Fernald Dosimetry Reconstruction Project and other sources to estimate the outdoor Rn exposure to individuals in the community immediately surrounding the FMPC during the years of plant operation. Using previously tabulated source terms, diffusion and meteorological data, and self-reported detailed residential histories, we estimated radon exposure for approximately 9300 persons who lived at more than 14,000 addresses. The results indicated that a portion of the population cohort experiences mean annual Rn exposure exceeding the U.S. Environmental Protection Agency (EPA) action limit of 4 pCiL⁻¹. These exposure estimates support the analysis of the incidence of lung cancer in the Fernald Community Cohort (FCC). Full article

(This article belongs to the Special Issue Atmospheric Radon Concentration Monitoring and Measurements (2nd Edition))

► Show Figures

Figure 1

6 pages, 169 KiB

Open AccessEditorial

Toward the Next-Generation of Heat-Health Warning Systems and Action Plans

by Andreas Matzarakis and Christos Giannaros

Atmosphere 2025, 16(8), 938; https://doi.org/10.3390/atmos16080938 - 5 Aug 2025

Abstract

As climate warming accelerates, heat emerges as a major planetary threat [...] Full article

(This article belongs to the Section Biometeorology and Bioclimatology)

16 pages, 2036 KiB

Open AccessArticle

Investigating a Characteristic Time Lag in the Ionospheric F-Region’s Response to Solar Flares

by Aisling N. O’Hare, Susanna Bekker, Harry J. Greatorex and Ryan O. Milligan

Atmosphere 2025, 16(8), 937; https://doi.org/10.3390/atmos16080937 - 5 Aug 2025

Abstract

X-ray and EUV solar flare emission cause increases in the Earth’s dayside ionospheric electron density. While the response of the lower ionosphere to X-rays is well studied, the delay between EUV flare emission and the response of the ionospheric F-region has not been [...] Read more.

X-ray and EUV solar flare emission cause increases in the Earth’s dayside ionospheric electron density. While the response of the lower ionosphere to X-rays is well studied, the delay between EUV flare emission and the response of the ionospheric F-region has not been investigated. Here, we calculate the delays between incident He II 304 Å emission, and the TEC response for 10 powerful solar flares, all of which exhibit delays under 1 min. We assess these delays in relation to multiple solar and geophysical factors, and find a strong negative correlation (∼−0.85) between delay and He II flux change and a moderate negative correlation (∼−0.55) with rate of increase in He II flux. Additionally, flare magnitude and the X-ray-to-He II flux ratio at peak He II emission show strong negative correlations with delay (∼−0.80 and ∼−0.75, respectively). We also identify longer delays for flares occurring closer to the summer solstice. These results may have applications in upper-ionospheric recombination rate calculations, atmospheric modelling, and other solar–terrestrial studies. We highlight the importance of incident EUV and X-ray flux parameters on the response time of the ionospheric electron content, and these findings may also have implications for mitigating disruptions in communication and navigation systems. Full article

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

► Show Figures

Figure 1

Journal Description

Atmosphere

Latest Articles

Journal Menu

Journal Browser

Highly Accessed Articles

Latest Books

E-Mail Alert

News

Topics

Conferences

Special Issues

Topical Collections

Further Information

Guidelines

MDPI Initiatives

Follow MDPI