Atmosphere

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Open AccessArticle

Weather Regimes of Extreme Wind Speed Events in Xinjiang: A 10–30 Year Return Period Analysis

by Yajie Li, Dagui Liu, Donghan Wang, Sen Xu, Bin Ma, Yueyue Yu, Jianing Li and Yafei Li

Atmosphere 2025, 16(10), 1117; https://doi.org/10.3390/atmos16101117 (registering DOI) - 24 Sep 2025

Abstract

Xinjiang is a critical wind energy region in China. This study characterizes extreme wind speed (EWS) events in Xinjiang by using ERA5 reanalysis (1979–2023) and station observations (2022–2023). Through k-means clustering and wind power density classification, four distinct regions and representative nodes were [...] Read more.

Xinjiang is a critical wind energy region in China. This study characterizes extreme wind speed (EWS) events in Xinjiang by using ERA5 reanalysis (1979–2023) and station observations (2022–2023). Through k-means clustering and wind power density classification, four distinct regions and representative nodes were identified, aligned with the “Three Mountains and Two Basins” topography: Region #1 (eastern wind-rich corridor), Region #2 (Tarim Basin, west–east increasing wind power density), Region #3 (northern valleys), and Region #4 (mountainous areas with weakest wind power density). Peaks-over-threshold analysis revealed 10~30-year return levels varying regionally, with 10-year return level for Node #1 reaching Beaufort Scale 11 but only Scale 6 for Node #4. Since 2001, EWS occurrences increased, with Nodes #2–4 showing doubled 10-year event occurrences in 2012–2023. Events exhibit consistent afternoon peaks and spring dominance (except Node #2 with summer maxima). Such long-term trends and diurnal and seasonal preferences of EWS could be partly explained by diverging synoptic drivers: orographic effects and enhanced pressure gradients (Node #1 and #3) associated with Ural blocking and polar vortex shifts, both showing intensification trends; thermal lows in the Tarim Basin (Node #2) accounting for their summer prevalence; boundary-layer instability that leads to localized wind intensification (Node #4). The results suggest the necessity of region-specific forecasting strategies for wind energy resilience. Full article

(This article belongs to the Special Issue Cutting-Edge Research in Severe Weather Forecast)

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Open AccessArticle

Spatiotemporal and Synoptic Analysis of PM₁₀ Based on Self-Organizing Map (SOM) During Asian Dust Events in South Korea

by Daekyeong Seong, JeongSeok Son, Dong-Ju Kim, Jongmin Yoon and Jae-Bum Lee

Atmosphere 2025, 16(10), 1116; https://doi.org/10.3390/atmos16101116 - 24 Sep 2025

Abstract

This study analyzes the spatiotemporal characteristics of PM₁₀ across 53 Asian dust events that affected the Korean Peninsula between January 2019 and June 2024. Self-Organizing Map (SOM) analysis was applied to sea level pressure and 850 hPa wind fields from the NCEP/DOE [...] Read more.

This study analyzes the spatiotemporal characteristics of PM₁₀ across 53 Asian dust events that affected the Korean Peninsula between January 2019 and June 2024. Self-Organizing Map (SOM) analysis was applied to sea level pressure and 850 hPa wind fields from the NCEP/DOE Reanalysis II dataset, classifying synoptic patterns into four distinct clusters. Cluster 1, associated with a deep low over Manchuria and strong westerly inflow, produced the highest PM₁₀ concentrations and the longest durations across most regions, with sharp afternoon peaks and the highest skewness values, and was mainly sourced from the Gobi Desert. Cluster 2 featured a high–low pressure dipole, generating localized impacts in northwestern regions and shorter durations, with moderate afternoon increases, originating primarily from the Gobi Desert and Inner Mongolia. Cluster 3, linked to a low east of Japan, resulted in elevated PM₁₀ mainly in central and southeastern regions, with peaks often occurring earlier in the day, and was associated with Manchurian dust sources. Cluster 4 exhibited a straight northwesterly flow with the high shifted eastward, producing moderate but spatially widespread concentrations and relatively consistent afternoon peaks, also linked to Manchurian sources. These results suggest that integrating synoptic pattern classification into dust forecasting can improve accuracy, enable early recognition of high-concentration events, and support the development of timely and region-specific warning strategies. Full article

(This article belongs to the Special Issue Atmospheric Aerosol Pollution)

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Open AccessArticle

Spatiotemporal Evolution of the Aridity Index and Its Latitudinal Patterns in the Lancang River Basin, China

by Liping Shan, Hangrui Zhang, Jingsheng Lei, Xiaojuan Ji, Xingji Zhu, Hang Yu and Long Wang

Atmosphere 2025, 16(10), 1115; https://doi.org/10.3390/atmos16101115 - 23 Sep 2025

Abstract

Under the context of global climate change, aridity responses exhibit significant differences across various latitudinal zones, and quantifying the dependency relationship between aridity and latitudinal zones is of great importance for differentiated water resource management. The Lancang River Basin in China spans 13 [...] Read more.

Under the context of global climate change, aridity responses exhibit significant differences across various latitudinal zones, and quantifying the dependency relationship between aridity and latitudinal zones is of great importance for differentiated water resource management. The Lancang River Basin in China spans 13 latitudinal zones with distinct altitudinal gradients, making it crucial to analyze the relationship between long-term aridity variation patterns and latitude for understanding basin hydrological response mechanisms. This study adopted the United Nations Environment Programme (UNEP) aridity index definition and utilized publicly available high-resolution datasets to divide the Chinese Lancang River Basin into 26 regions at 0.5° N intervals. The spatiotemporal evolution characteristics of the aridity index at interannual and seasonal scales from 1940 to 2022 were analyzed, and the trends of aridity index changes and their relationship with latitude were quantified. Results indicate: (1) The spring aridity index increased significantly (trend rate of 0.015/10a, Z = 2.39), driving an overall basin-wide humidification trend. (2) The aridity index exhibited significant spatial and seasonal differences with latitude: southern regions (south of 24.75° N) showed negative correlations, northern regions (north of 30.5° N) showed positive correlations, while central regions displayed distinct seasonal transitions and spatial differentiation characteristics bounded by 27.25° N. (3) The rate of aridity index change in regions north of 27.25° N was significantly higher than in southern regions (p < 0.001). This study reveals the latitudinal patterns of AI changes in the Lancang River Basin, providing guidance for developing adaptive water resource allocation strategies under climate change scenarios. Full article

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

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Open AccessArticle

Concentration Distribution and Physicochemical Properties of 10 nm–10 μm Coal Dust Generated by Drum Cutting Different Rank Coals: A Physical Simulation Experiment

by Hui Liu, Rong Jia, Jintuo Zhu, Liang Wang, Jiamu Tong, Yu Liu, Qingyang Tian, Wenbo Liu, Caixia An and Nkansah Benjamin Oduro

Atmosphere 2025, 16(10), 1114; https://doi.org/10.3390/atmos16101114 - 23 Sep 2025

Abstract

Shearer drum cutting of coal seams generates over half of the coal dust in coal mines, while relevant studies focus more on micron-sized dust and much less on nano- to sub-micron-sized coal dust. Based on the self-developed experimental system for simulating dust generation [...] Read more.

Shearer drum cutting of coal seams generates over half of the coal dust in coal mines, while relevant studies focus more on micron-sized dust and much less on nano- to sub-micron-sized coal dust. Based on the self-developed experimental system for simulating dust generation from drum cutting of coal bodies, this study investigated the concentration distribution characteristics and physicochemical properties of 10 nm–10 μm coal dust generated from drum cutting of different rank coals with different cutting parameters. Results showed that the coal dust mass and number concentrations were concentrated in 2–10 μm and 10–200 nm, respectively, accounting for 90% of the total 10 nm–10 μm coal dust; the mass percentages of PM1/PM10 (PM1/PM10 = PM1 particles relative to PM10 particles, similarly hereinafter), PM1/PM2.5, and PM2.5/PM10 were 3.25–4.87%, 19.35–26.73%, and 14.82–18.81%, respectively, whereas over 99% of the total number of particles in the PM10 fraction are within the PM1 fraction (i.e., N-PM1/N-PM10 > 99%), that is, both N-PM1/N-PM2.5 and N-PM2.5/N-PM10 exceeded 99%. Lower-rank coal generates less 10 nm–10 μm coal dust, and either higher moisture content, firmness coefficient, or lower fixed carbon content of the coal can effectively reduce the 10 nm–10 μm coal dust generation. Either reduction in the tooth tip cone angle, the rotary speed, or increase in the mounting angle or the cutting depth can effectively inhibit the 10 nm–10 μm coal dust generation. Higher-rank coal dust shows fewer surface pores, smoother surfaces, larger contact angles, more hydrophobic groups, and fewer hydrophilic groups. The research results have filled the knowledge gap in the pollution characteristics of nano- to submicron-sized dust generated from shearer drum cutting of coal bodies, and can serve as an important reference for the development of dust reduction and suppression technologies in coal mining faces as well as the prevention of coal worker’s pneumoconiosis. Full article

(This article belongs to the Section Air Quality)

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Open AccessArticle

Natural Variability and External Forcing Factors That Drive Surface Air Temperature Trends over East Asia

by Debashis Nath, Reshmita Nath and Wen Chen

Atmosphere 2025, 16(10), 1113; https://doi.org/10.3390/atmos16101113 - 23 Sep 2025

Abstract

Community Earth System Model-Large Ensemble (CESM-LE) simulations are used to partition the Surface Air Temperature (SAT) trends over East Asia into the contribution of external forcing factors and internal variability. In the historical period (1966–2005), the summer SAT trends display a considerable diversity [...] Read more.

Community Earth System Model-Large Ensemble (CESM-LE) simulations are used to partition the Surface Air Temperature (SAT) trends over East Asia into the contribution of external forcing factors and internal variability. In the historical period (1966–2005), the summer SAT trends display a considerable diversity (≤−2 °C to ≥2 °C) across the 35 member ensembles, while under the RCP8.5 scenario, the region is mostly dominated by a strong warming trend (~1.5–2.5 °C/51 years) and touches the ~4 °C mark by the end of the 21st century. In the historical period, the warming is prominent over the Yangtze River basin of China, while under the RCP8.5 scenario, the warming pattern shifts northward towards Mongolia. In the historical period, the Signal-to-Noise Ratio (SNR) is less than 1, while it is higher than 4 under the RCP8.5 scenario, which indicates that, in the early period, internal variability overrides the forced response and vice versa under the RCP8.5 scenario. In addition, over much of the East Asian region, the chances of cooling are relatively high in the historical period, which partially counteracted the warming trend due to external forcing factors. In contrast, under the RCP8.5 scenario, the chances of warming reach ~100% over East Asia due to contributions from the external forcing factors. The novel aspect of the current study is that, in the negative phase (from the mid-1960s to ~2000), the Atlantic Multidecadal Oscillation (AMO) accounts for ~70–80% of the cooling trend or the SAT variability over East Asia, and thereafter, natural variability exhibits a slow increasing trend in the future. However, the contribution of external forcing factors increases from ~55% in 2000 to 95% in 2075 at a rate much faster than natural variability, which is primarily due to increasing downward solar radiation fluxes and albedo feedback on SAT over East Asia. Full article

(This article belongs to the Special Issue Tropical Monsoon Circulation and Dynamics)

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17 pages, 4683 KB

Open AccessArticle

Contrast Between Automated and Manual Measurements of Atmospheric PM_2.5: Influences of Environmental Factors and the Improving Correction Method

by Dongjue Dai, Jingang Li, Kuang Xiao and Li Li

Atmosphere 2025, 16(9), 1112; https://doi.org/10.3390/atmos16091112 - 22 Sep 2025

Abstract

In this work, we tested the performance of automated atmospheric PM_2.5 monitoring instruments and contrasted the data from automated measurements with those from filter-based reference measurements. The tested instruments include four brands of beta attenuation instruments (two were made in China, D1 [...] Read more.

In this work, we tested the performance of automated atmospheric PM_2.5 monitoring instruments and contrasted the data from automated measurements with those from filter-based reference measurements. The tested instruments include four brands of beta attenuation instruments (two were made in China, D1 and D2; the other two were imported from other countries, I1 and I2) and one brand of a light scattering instrument (also imported from another country, I3). The automated monitoring data were corrected based on the reference tests. The total testing period lasted 18 months. The objective of this work is to evaluate the influences of environmental factors on the performance of different automated instruments, and to improve the accuracy of the automated instruments by using a correction method. The results showed that contrasted with the reference tests, the absolute errors (MAE, mean absolute error; SD, standard deviation; and RMSE, root mean square error) of the automated monitoring instruments werehigher for temperature (T ≤ 10 °C), humidity (60% ≤ RH < 80%), and PM_2.5 concentrations (PM_2.5 ≥ 75 μg/m³). Meanwhile, the relative errors (CV, coefficient of variation; and NRMSE, normalized root mean square error) of the automated monitoring instruments were higher for humidity (RH > 80%) and PM_2.5 concentrations (PM_2.5 < 15 μg/m³). For winter data, it proved challenging to pass the reference test, which was based on a linear regression between 24-h average automated monitoring data and the integrated filter-based PM_2.5 data (aka the KBR test). Before corrections, the pass rates of D1, D2, I1, I2, and I3 in the rolling KBR tests are 57.7%, 51.3%, 41.1%, 21%, and 90.2%, respectively. After corrections, the rates increase to 79.6%, 86.6%, 81.8%, 58.9%, and 91.8%, respectively. The coefficient corrections (corrections of system errors) have made the most prominent contribution to improving the pass rates of the winter samples. The quarterly correction method can significantly improve the data accuracy of automated monitoring instruments. Full article

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

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19 pages, 3297 KB

Open AccessArticle

Spatiotemporal Dynamic Evolution Characteristics of Net Carbon Sinks in County-Level Farmland Ecosystems in Hunan Province, China

by Huangling Gu, Yuqi Chen, Jiaoruo Ding, Haoyang Xin, Yan Liu and Lin Li

Atmosphere 2025, 16(9), 1111; https://doi.org/10.3390/atmos16091111 - 22 Sep 2025

Abstract

A quantitative study on the spatial structure and spatiotemporal variation characteristics of net carbon sinks in regional farmland ecosystems is of significant importance for uncovering the multifunctional roles of farmland ecosystems and formulating region-specific agricultural policies and management strategies. Based on the measurement [...] Read more.

A quantitative study on the spatial structure and spatiotemporal variation characteristics of net carbon sinks in regional farmland ecosystems is of significant importance for uncovering the multifunctional roles of farmland ecosystems and formulating region-specific agricultural policies and management strategies. Based on the measurement of net carbon sinks in county-level farmland ecosystems across Hunan Province from 2005 to 2020, this research employs methodologies, including the standard deviational ellipse (SDE), spatial autocorrelation, and exploratory spatiotemporal data analysis (ESTDA) to investigate the spatiotemporal evolution characteristics of net carbon sinks in Hunan’s county-level farmland ecosystems. The results show that the net carbon sinks of county-level farmland ecosystems in Hunan Province exhibits a “northeast–southwest” spatial distribution pattern, with a trend toward spatial agglomeration during contraction, and the center of gravity of net carbon sinks has generally shifted northwestward over time. A significant positive spatial correlation exists globally in the net carbon sinks of Hunan’s county-level farmland ecosystems, and the degree of spatial agglomeration has gradually intensified amid fluctuations. The dynamic evolution of local spatial patterns of net carbon sinks in county-level farmland ecosystems in Hunan Province varied significantly, showing strong stability in both local spatial structure and spatial dependence direction. In contrast, eastern and central Hunan exhibited more dynamic local spatial structures compared to southern and northern regions. The local spatial association patterns of the net carbon sinks in county-level farmland ecosystems remained relatively stable, with weak spatial synergy and a pronounced path-dependent locking effect in spatial agglomeration. Full article

(This article belongs to the Section Biometeorology and Bioclimatology)

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27 pages, 9431 KB

Open AccessArticle

Improved Monthly Frequency Method Based on Copula Functions for Studying Ecological Flow in the Hailang River Basin, Northeast China

by Zijun Wang, Yusu Zhao, Jian Shang, Yuanming Wang, Changlei Dai and Enzhong Li

Atmosphere 2025, 16(9), 1110; https://doi.org/10.3390/atmos16091110 - 22 Sep 2025

Abstract

Climate change has intensified extreme hydrological events in cold regions, threatening the stability of river ecosystems. The traditional monthly frequency method for calculating ecological flow assumes equal guarantee rates across all months, overlooking the complex nonlinear dependencies between interannual and intermonthly flows. This [...] Read more.

Climate change has intensified extreme hydrological events in cold regions, threatening the stability of river ecosystems. The traditional monthly frequency method for calculating ecological flow assumes equal guarantee rates across all months, overlooking the complex nonlinear dependencies between interannual and intermonthly flows. This approach may result in flow values for certain months during low-flow years exceeding those of corresponding months in high-flow years, failing to align with actual hydrological patterns. This study integrates Copula functions with the monthly frequency method to establish an improved ecological flow calculation framework, accurately characterizing the statistical correlation between interannual and intermonthly flow variability. The Hailang River basin in Northeast China was selected as the study area. First, the SWAT model was employed to simulate natural runoff processes from 1956 to 1965. The calibration phase demonstrated excellent performance (R² = 0.84, NSE = 0.83), and the validation phase also met standards (R² = 0.82, NSE = 0.81). The improved method selected optimal Copula functions for each month through rigorous statistical tests (AIC, BIC, RMSE, and K-S test), establishing joint probability distributions for annual and monthly average flows. The results indicate that different Copula types better align with monthly hydrological seasonal characteristics: Gaussian Copula suits February, May, and July; t-Copula suits August; Clayton Copula from September to December; Gumbel Copula for January, March, April, and June. Through conditional probability relationships (P(X₀≥x₀, 90%) = 0.9), the monthly guarantee rate range determined by the improved method spans 81.83% to 90.08%, significantly outperforming the uniform 90% guarantee rate employed by traditional methods. Verification using the Tennant method confirmed that ecological flows throughout the year met “excellent” or higher standards. Ecological flows exhibited pronounced seasonal variation, ranging from 6.2 m³/s during winter to spring to 96.93 m³/s during summer to autumn, providing scientific basis for basin-scale ecological water management. This study establishes a reliable methodological framework for ecological flow management in cold-region rivers. Full article

(This article belongs to the Special Issue Water Resource Challenges and Sustainable Management Solutions Under the Interaction of Climate Change and Human Activities)

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17 pages, 4643 KB

Open AccessArticle

Deep Learning Emulator Towards Both Forward and Adjoint Modes of Atmospheric Gas-Phase Chemical Process

by Yulong Liu, Meicheng Liao, Jiacheng Liu and Zhen Cheng

Atmosphere 2025, 16(9), 1109; https://doi.org/10.3390/atmos16091109 - 21 Sep 2025

Abstract

Gas-phase chemistry has been identified as a major computational bottleneck in both the forward and adjoint modes of chemical transport models (CTMs). Although previous studies have demonstrated the potential of deep learning models to simulate and accelerate this process, few studies have examined [...] Read more.

Gas-phase chemistry has been identified as a major computational bottleneck in both the forward and adjoint modes of chemical transport models (CTMs). Although previous studies have demonstrated the potential of deep learning models to simulate and accelerate this process, few studies have examined the applicability and performance of these models in adjoint sensitivity analysis. In this study, a deep learning emulator for gas-phase chemistry is developed and trained on a diverse set of forward-mode simulations from the Community Multiscale Air Quality (CMAQ) model. The emulator employs a residual neural network (ResNet) architecture referred to as FiLM-ResNet, which integrates Feature-wise Linear Modulation (FiLM) layers to explicitly account for photochemical and non-photochemical conditions. Validation within a single timestep indicates that the emulator accurately predicts concentration changes for 74% of gas-phase species with coefficient of determination (R²) exceeding 0.999. After embedding the emulator into the CTM, multi-timestep simulation over one week shows close agreement with the numerical model. For the adjoint mode, we compute the sensitivities of ozone (O₃) with respect to O₃, nitric oxide (NO), nitrogen dioxide (NO₂), hydroxyl radical (OH) and isoprene (ISOP) using automatic differentiation, with the emulator-based adjoint results achieving a maximum R² of 0.995 in single timestep evaluations compared to the numerical adjoint sensitivities. A 24 h adjoint simulation reveals that the emulator maintains spatially consistent adjoint sensitivity distributions compared to the numerical model across most grid cells. In terms of computational efficiency, the emulator achieves speed-ups of 80×–130× in the forward mode and 45×–102× in the adjoint mode, depending on whether inference is executed on Central Processing Unit (CPU) or Graphics Processing Unit (GPU). These findings demonstrate that, once the emulator is accurately trained to reproduce forward-mode gas-phase chemistry, it can be effectively applied in adjoint sensitivity analysis. This approach offers a promising alternative approach to numerical adjoint frameworks in CTMs. Full article

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

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11 pages, 701 KB

Open AccessCommentary

Air and Surface Purification Using Heterogeneous Photocatalysis: Enhanced Indoor Sanitisation Through W₁₈O₄₉ and ZnO Catalyst Systems

by Pablo Fernandez, Wesley Paul and Prashant Kumar

Atmosphere 2025, 16(9), 1108; https://doi.org/10.3390/atmos16091108 - 21 Sep 2025

Abstract

Indoor air quality management has become increasingly critical for public health, particularly after the global COVID-19 respiratory disease outbreaks that highlighted airborne pathogen transmission risks. This review investigates an advanced air and surface purification method that is used in devices utilising heterogeneous photocatalysis [...] Read more.

Indoor air quality management has become increasingly critical for public health, particularly after the global COVID-19 respiratory disease outbreaks that highlighted airborne pathogen transmission risks. This review investigates an advanced air and surface purification method that is used in devices utilising heterogeneous photocatalysis with tungsten oxide (W₁₈O₄₉) and zinc oxide (ZnO) catalyst systems to generate controlled concentrations of hydrogen peroxide for continuous indoor sanitisation. The photocatalytic system converts ambient water vapour into aerosolised hydrogen peroxide at concentrations of 0.04–0.05 ppm, significantly below established safety thresholds, while maintaining effective antimicrobial activity. The W₁₈O₄₉ catalyst demonstrates superior visible-light absorption compared to conventional titanium dioxide (TiO₂) systems, with ZnO serving as an effective cocatalyst to reduce electron–hole recombination and enhance reactive oxygen species generation. Safety analysis based on OSHA, WHO, and ACGIH guidelines confirms that continuous exposure to these low hydrogen peroxide concentrations poses no health risks to occupants. Real-world applications demonstrate up to 90% reduction in airborne pathogens and a 20–30% decrease in sick leave rates in office environments. The technology offers significant economic benefits through reduced healthcare costs and improved productivity while providing environmentally sustainable air purification without harmful residues. This photocatalytic approach represents a scientifically validated, safe, and economically viable solution for next-generation indoor air quality management across healthcare, educational, commercial, and residential sectors. Full article

(This article belongs to the Section Air Quality)

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29 pages, 2906 KB

Open AccessArticle

Spatiotemporal Graph Convolutional Network-Based Long Short-Term Memory Model with A* Search Path Navigation and Explainable Artificial Intelligence for Carbon Monoxide Prediction in Northern Cape Province, South Africa

by Israel Edem Agbehadji and Ibidun Christiana Obagbuwa

Atmosphere 2025, 16(9), 1107; https://doi.org/10.3390/atmos16091107 - 21 Sep 2025

Abstract

Background: The emission of air pollutants into the atmosphere is a global issue as it contributes to global warming and climate-related issues. Human activities like the burning of fossil fuel influence changes in weather patterns—resulting in issues such as a rise in sea [...] Read more.

Background: The emission of air pollutants into the atmosphere is a global issue as it contributes to global warming and climate-related issues. Human activities like the burning of fossil fuel influence changes in weather patterns—resulting in issues such as a rise in sea levels, among other things. Identifying road network routes within Northern Cape Province in South Africa that are less exposed to air pollutants like carbon monoxide is the issue this study seeks to address. Methods: The method used for our predictions is based on a graph convolutional network (GCN) and long short-term memory (LSTM). The GCN extracts geospatial characteristics, and the LSTM captures both nonlinear relationships and temporal dependencies in an air pollutant and meteorological dataset. Furthermore, an A* search strategy identifies the path from one location to another with the lowest carbon monoxide concentrations within a road network. The explainable artificial intelligence (xAI) technique is used to describe the nonlinear relationship between the target variable and features. Meteorological and air pollutant data in the form of statistical mean, minimum, and maximum values were leveraged, and a random sampling technique was utilized to fill the data gap to help train the predictive model (GCN-LSTM-A*). Results: The predictive model was evaluated with mean squared error (MSE) and root mean squared error (RMSE) values within two multi-time steps (8 and 16 h) with MSEs of 0.1648 and 0.1701, respectively. The LIME technique, which provides explanations of features, shows that Wind_speed and NO₂ and NOx concentrations decreased the predicted CO, whereas PM_2.5, PM₁₀, relative humidity, and O₃ increased the predicted CO of the route. Full article

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

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17 pages, 836 KB

Open AccessArticle

The Time Delays in Reaction of the Ionosphere and the Earth’s Magnetic Field to the Solar Flares on 8 May and Geomagnetic Superstorm on 10 May 2024

by Nazyf Salikhov, Alexander Shepetov, Galina Pak, Serik Nurakynov, Vladimir Ryabov, Zhumabek Zhantayev and Valery Zhukov

Atmosphere 2025, 16(9), 1106; https://doi.org/10.3390/atmos16091106 - 20 Sep 2025

Abstract

In the paper we consider the pulsed disturbances caused in the ionosphere by an extreme G5-level geomagnetic superstorm on 10 May 2024, and by the X1.0 and M-class solar flares on 8 May 2024, which preceded the storm. Particular attention is [...] Read more.

In the paper we consider the pulsed disturbances caused in the ionosphere by an extreme G5-level geomagnetic superstorm on 10 May 2024, and by the X1.0 and M-class solar flares on 8 May 2024, which preceded the storm. Particular attention is paid to the short-term delays and the sequence of disturbance appearance in the ionosphere and geomagnetic field during these extreme events. The results of a continuous Doppler sounding of the ionosphere on an inclined radio path with a sampling frequency of 25 Hz were used, as well as the data of a ground-based mid-latitude fluxgate magnetometer LEMI-008, and an induction magnetometer IMS-008, which operated with a sampling frequency of 66.6 Hz. Ionization of the ionosphere by the intense X-ray and extreme ultraviolet radiation of solar flares was accompanied by the equally sudden and similarly timed disturbances in the Doppler frequency shift (DFS) of the ionospheric signal, which had an amplitude of 2.0–5.8 Hz. The largest pulsed burst in DFS was registered 68 s after an X1.0 flare on 8 May 2024 at the time when the change of the X-ray flux was at its maximum. Following onto the effect in the ionosphere, a disturbance in the geomagnetic field appeared with a time delay of 35 s. This disturbance is a secondary one that arose as a consequence of the ionosphere response to the solar flare. It was likely driven by the contribution of ionospheric currents and electric fields, which modified the Earth’s magnetic field. On 10 May 2024, a G5-level geomagnetic superstorm with a sudden commencement triggered an impulsive reaction in the ionosphere. A response in DFS at the calculated reflection altitude of the sounding radio wave of 267.5 km was detected 58 s after the commencement of the storm. The sudden impulsive changes in Doppler frequencies showed a bipolar character, reflecting complex dynamic transformations in the ionosphere at the geomagnetic storm. Consequently, the DFS amplitude initially rose to 5.5 Hz over 86 s, and then its sharp drop to

- 3.2

Hz followed. Using the instruments that operated in a mode with a high temporal resolution allowed us to identify for the first time the impulsive nature of the ionospheric reaction, the time delays, and the sequence of disturbance appearances in the ionosphere and geomagnetic field in response to the X1.0 solar flare on 8 May 2024 as well as to the sudden commencement of the extreme G5-level geomagnetic storm on 10 May 2024. Full article

(This article belongs to the Section Upper Atmosphere)

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18 pages, 7743 KB

Open AccessArticle

Improved Daytime Cloud Detection Algorithm in FY-4A’s Advanced Geostationary Radiation Imager

by Xiao Zhang, Song-Ying Zhao and Rui-Xuan Tang

Atmosphere 2025, 16(9), 1105; https://doi.org/10.3390/atmos16091105 - 20 Sep 2025

Abstract

Cloud detection is an indispensable step in satellite remote sensing of cloud properties and objects under the influence of cloud occlusion. Nevertheless, interfering targets such as snow and haze pollution are easily misjudged as clouds for most of the current algorithms. Hence, a [...] Read more.

Cloud detection is an indispensable step in satellite remote sensing of cloud properties and objects under the influence of cloud occlusion. Nevertheless, interfering targets such as snow and haze pollution are easily misjudged as clouds for most of the current algorithms. Hence, a robust cloud detection algorithm is urgently needed, especially for regions with high latitudes or severe air pollution. This paper demonstrated that the passive satellite detector Advanced Geosynchronous Radiation Imager (AGRI) onboard the FY-4A satellite has a great possibility to misjudge the dense aerosols in haze pollution as clouds during the daytime, and constructed an algorithm based on the spectral information of the AGRI’s 14 bands with a concise and high-speed calculation. This study adjusted the previously proposed cloud mask rectification algorithm of Moderate-Resolution Imaging Spectroradiometer (MODIS), rectified the MODIS cloud detection result, and used it as the accurate cloud mask data. The algorithm was constructed based on adjusted Fisher discrimination analysis (AFDA) and spectral spatial variability (SSV) methods over four different underlying surfaces (land, desert, snow, and water) and two seasons (summer and winter). This algorithm divides the identification into two steps to screen the confident cloud clusters and broken clouds, which are not easy to recognize, respectively. In the first step, channels with obvious differences in cloudy and cloud-free areas were selected, and AFDA was utilized to build a weighted sum formula across the normalized spectral data of the selected bands. This step transforms the traditional dynamic-threshold test on multiple bands into a simple test of the calculated summation value. In the second step, SSV was used to capture the broken clouds by calculating the standard deviation (STD) of spectra in every 3 × 3-pixel window to quantify the spectral homogeneity within a small scale. To assess the algorithm’s spatial and temporal generalizability, two evaluations were conducted: one examining four key regions and another assessing three different moments on a certain day in East China. The results showed that the algorithm has an excellent accuracy across four different underlying surfaces, insusceptible to the main interferences such as haze and snow, and shows a strong detection capability for broken clouds. This algorithm enables widespread application to different regions and times of day, with a low calculation complexity, indicating that a new method satisfying the requirements of fast and robust cloud detection can be achieved. Full article

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

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16 pages, 4477 KB

Open AccessArticle

Forecasting ⁷Be Concentrations Using Time Series Analysis: A Case Study of Panama City

by Alexander Esquivel-López, Bernardo Fernández, Omayra Pérez, Felipe Castillo, Nathalia Tejedor-Flores and Mitzi Cubilla-Montilla

Atmosphere 2025, 16(9), 1104; https://doi.org/10.3390/atmos16091104 - 20 Sep 2025

Abstract

Beryllium-7 (⁷Be) is widely used as an atmospheric radiotracer due to its short half-life and ease of detection. Its evaluation and forecasting provide valuable insights into atmospheric behavior and environmental processes. This study aimed to develop a robust explanatory and predictive [...] Read more.

Beryllium-7 (⁷Be) is widely used as an atmospheric radiotracer due to its short half-life and ease of detection. Its evaluation and forecasting provide valuable insights into atmospheric behavior and environmental processes. This study aimed to develop a robust explanatory and predictive model for ⁷Be concentrations in Panama using monthly data from 2006 to 2019 provided by the RN50 Station at the University of Panama. This study employed ARIMA models for time series analysis and forecasting, complemented by error metrics such as Root Mean Squared Error (RMSE), Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) to assess the accuracy of the results. After verifying data suitability, analyzing series components, and testing stationarity using the Dickey–Fuller test, the SARIMA (2,0,1) (2,1,0) model was identified as optimal. This model successfully forecasted ⁷Be concentrations for the final five months of 2019, offering a useful tool for understanding airborne particle dynamics in Panama and supporting future applications of ⁷Be in the study and estimation of soil erosion. Full article

(This article belongs to the Section Air Quality)

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25 pages, 2387 KB

Open AccessArticle

Application of Low-Cost Air Quality Monitoring System in Educational Facilities in Belgrade, Serbia

by Uzahir Ramadani, Slobodan Radojević, Ivan M. Lazović, Dušan S. Radivojević, Jelena Obradović, Marija Živković and Viša Tasić

Atmosphere 2025, 16(9), 1103; https://doi.org/10.3390/atmos16091103 - 19 Sep 2025

Abstract

Indoor and outdoor air quality in school environments varies significantly with respect to particulate matter (PM) concentrations, carbon dioxide (CO₂) levels, and microclimatic conditions, all of which have a direct impact on the health, well-being, and performance of both students and [...] Read more.

Indoor and outdoor air quality in school environments varies significantly with respect to particulate matter (PM) concentrations, carbon dioxide (CO₂) levels, and microclimatic conditions, all of which have a direct impact on the health, well-being, and performance of both students and staff. This study reports the findings of a monitoring campaign focused on PM₁₀ and PM_2.5 concentrations in two schools located in the urban area of Belgrade, Serbia. Measurements were carried out using low-cost sensor devices positioned in classrooms and in the surrounding outdoor environment. The PM concentration data were corrected through collocation with reference-grade automatic analyzers (Grimm EDM 180) from the National Air Quality Monitoring Network (NAQMN). During the winter season, the indoor-to-outdoor (I/O) concentration ratio for classrooms ranged between 0.7 and 0.8, indicating that indoor PM levels were generally lower than outdoor levels—likely a result of limited ventilation and reduced particle infiltration from outdoor sources. Conversely, in the summer season, the average I/O ratio typically exceeded 1.0 (ranging from 1.3 to 1.5), pointing to a more pronounced influence of indoor sources, such as occupant activities, resuspension of settled dust, and insufficient air exchange. Importantly, in over 60% of the measurements conducted during the summer period, indoor PM concentrations surpassed those outdoors, underscoring the critical need to address indoor emission sources and implement effective ventilation strategies, particularly during warmer months. Full article

(This article belongs to the Section Air Quality)

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18 pages, 7038 KB

Open AccessReview

Advancing Nature-Based Solutions with Artificial Intelligence: A Bibliometric and Semantic Analysis Using ChatGPT

by Mo Wang, Hui Liu, Menghan Zhang and Rana Muhammad Adnan

Atmosphere 2025, 16(9), 1102; https://doi.org/10.3390/atmos16091102 - 18 Sep 2025

Abstract

In response to escalating climate change and ecological degradation, nature-based solutions (NBSs) have emerged as a critical paradigm for sustainable environmental governance. Simultaneously, artificial intelligence (AI) offers powerful capabilities for addressing the complexity and uncertainty inherent in natural systems. This study investigates the [...] Read more.

In response to escalating climate change and ecological degradation, nature-based solutions (NBSs) have emerged as a critical paradigm for sustainable environmental governance. Simultaneously, artificial intelligence (AI) offers powerful capabilities for addressing the complexity and uncertainty inherent in natural systems. This study investigates the integration of AI within NBS through a hybrid bibliometric and semantic-enhancement framework. Drawing on 535 peer-reviewed articles from the Web of Science Core Collection (2011–2024), we employ keyword co-occurrence analysis via CiteSpace and semantic refinement using ChatGPT-4.0 to identify 15 key thematic clusters. Results reveal that AI is widely applied in ecological monitoring, carbon emission reduction, urban climate adaptation, and green infrastructure optimization—substantially improving the responsiveness, precision, and scalability of NBS interventions. The proposed methodology enhances both structural insight and semantic coherence in bibliometric review, offering a robust foundation for future interdisciplinary research. This study contributes to the theoretical development and practical implementation of AI-enhanced NBS, supporting data-driven, adaptive strategies for climate resilience and sustainable development. Full article

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

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16 pages, 2811 KB

Open AccessArticle

Diagnostic Ratios and Directional Analysis of Air Pollutants for Source Identification: A Global Perspective with Insights from Kuwait

by Abdullah N. Al-Dabbous

Atmosphere 2025, 16(9), 1101; https://doi.org/10.3390/atmos16091101 - 18 Sep 2025

Abstract

Identifying the sources of atmospheric pollutants is essential for effective air quality management. This study assesses the diagnostic value of SO₂/NO₂ and CO/NO₂ ratios in distinguishing between major emission sources, including vehicular traffic, industrial activity, and biomass burning. A [...] Read more.

Identifying the sources of atmospheric pollutants is essential for effective air quality management. This study assesses the diagnostic value of SO₂/NO₂ and CO/NO₂ ratios in distinguishing between major emission sources, including vehicular traffic, industrial activity, and biomass burning. A global literature review was conducted to establish typical ratio thresholds associated with different sources. These thresholds were then applied in a case study of Kuwait, a representative Gulf Cooperation Council country with intense vehicular traffic and industrial activity. To complement the ratio-based diagnostics, directional pollution source identification was performed using the Conditional Bivariate Probability Function (CBPF) plots, linking elevated pollutant concentrations to prevailing wind speeds/directions. Results indicate that Al-Fahaheel exhibits a distinct SO₂/NO₂ ratio toward the south-southeast due to industrial activities, and a pronounced CO/NO₂ ratio toward the east, reflecting contributions from mixed urban and traffic-related sources. The observed ratios at the Al-Fahaheel air quality monitoring station—very low CO/NO₂ and moderate to high SO₂/NO₂—are inconsistent with vehicular emissions and are more indicative of industrial emissions from stationary sources. Directional CBPF plots reinforce these associations by clearly linking industrial activities and vehicular traffic sources to the southeastern and western sectors, respectively. Full article

(This article belongs to the Section Air Quality)

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15 pages, 3831 KB

Open AccessArticle

Air Quality Response to COVID-19 Control Measures in the Arid Inland Region of China: A Case Study of Eastern Xinjiang

by Hui Xu, Yuanyuan Zhang, Yunhui Zhang, Bo Cao, Zihang Qin, Xiaofang Zhou, Li Zhang and Mingjie Xie

Atmosphere 2025, 16(9), 1100; https://doi.org/10.3390/atmos16091100 - 18 Sep 2025

Abstract

This study examined the temporal changes and dispersion of potential sources of the six criteria air pollutants, namely, particulate matter with an aerodynamic diameter of less than 2.5 and 10 μm (PM_2.5 and PM₁₀), nitrogen dioxide (NO₂), sulfur [...] Read more.

This study examined the temporal changes and dispersion of potential sources of the six criteria air pollutants, namely, particulate matter with an aerodynamic diameter of less than 2.5 and 10 μm (PM_2.5 and PM₁₀), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), carbon monoxide (CO), and ozone (O₃), in eastern Xinjiang, China, during the COVID-19 period in summer 2020 (16 July to 29 August ). Compared to the same periods in 2019 and 2021, the mean concentrations of all pollutants, except for SO₂ and O₃, and the air quality index (AQI) were lower in 2020 (relative changes: NO₂ 48.3–54.4%, PM₁₀ 35.8–49.6%, PM_2.5 19.3–43.5%, CO 16.5–34.8%, AQI 17.2–29.4%), which can be attributed to the reduced anthropogenic activities. Compared to the period before the lockdown in 2020 (16 June to 15 July), the mean NO₂ concentration showed the largest decrease during the lockdown (47.9%), followed by PM_2.5 (32.7%), PM₁₀ (37.6%), and CO (15.4%). In contrast, there were only minimal changes in O₃, with the mean concentrations falling slightly by 7.56%, and the mean concentration of SO₂ increased by 10.4%. The decrease in NO_x and the dry climate could have hindered O₃ formation, while vital industrial activities in eastern Xinjiang probably maintained SO₂ emissions. In the subsequent recovery period (30 August to 28 September), the mean NO₂ concentration increased the most at 59.3%, which was due to the rapid resumption of traffic-related emissions. During the lockdown in 2020, the diurnal profiles of PM_2.5, PM₁₀, NO₂, and CO concentrations showed lower peak concentrations in the morning (09:00–11:00) and evening (20:00–22:00), demonstrating a significant reduction in traffic-related emissions. The lower O₃ and higher SO₂ peak concentrations may have resulted from lower NO_x levels and higher electricity consumption due to the “stay-at-home” policy. The analysis of the distribution of potential sources showed that O₃ generally originated from widespread source areas, while the other pollutants mainly originated from local emissions. During the lockdown period, the source areas of PM_2.5 and PM₁₀ were more dispersed, with an enhanced contribution from long-range transport. Full article

(This article belongs to the Section Air Quality)

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22 pages, 13233 KB

Open AccessArticle

Severe Typhoon Danas (2025)—A Tropical Cyclone with Erratic Track over the Northern Part of the South China Sea and Adjacent Sea of Taiwan

by Chun-Wing Choy, Pak-Wai Chan, Ping Cheung, Ching-Chi Lam, Chun-Kit Ho, Yu-Heng He and Jun-Yi He

Atmosphere 2025, 16(9), 1099; https://doi.org/10.3390/atmos16091099 - 18 Sep 2025

Abstract

Severe Typhoon Danas over the northern part of the South China Sea and seas near Taiwan in early July 2025 had an erratic path that had not been observed before, according to historical data in the region. Its formation, movement, and intensification posed [...] Read more.

Severe Typhoon Danas over the northern part of the South China Sea and seas near Taiwan in early July 2025 had an erratic path that had not been observed before, according to historical data in the region. Its formation, movement, and intensification posed significant challenges to the timely tropical cyclone (TC) warning services. This paper documents the observational aspect and forecasting aspect of this cyclone. There are key findings: (a) when Danas interacted with the Central Mountain Range of Taiwan, a “secondary cyclone” appeared over the northeastern part of Taiwan, which was observed by both weather radars and meteorological satellite winds, and was simulated to a certain extent by a mesoscale numerical weather prediction (NWP) model; (b) data-driven AI global models performed better than physics-based global NWP models in capturing the formation and the rather erratic track of Danas a couple of days earlier, although AI models generally underestimate the intensity forecasts; and (c) an atmosphere–ocean–wave coupled model was found to perform the best in capturing both the track changes of Danas (because of being driven by an AI global model) and its intensity changes (because of better physical representation of the oceanic impact on the intensity of this TC), whereas AI global models, though with various recent enhancements, still tended to underestimate the strength of Danas. This paper serves as a reference of this rather unusual TC for the weather forecasting services in the region. Full article

(This article belongs to the Special Issue Typhoon Climatology: Intensity and Structure)

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