Atmosphere

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

Viewed by 197

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

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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

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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

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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

Viewed by 245

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

Viewed by 253

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

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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

Viewed by 227

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

Viewed by 530

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

Viewed by 361

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

Viewed by 376

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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15 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

Viewed by 330

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

Viewed by 160

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

Viewed by 760

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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19 pages, 2251 KB

Open AccessArticle

Study on the Influence of Topography on Dew Amount—A Case Study of Hilly and Gully Regions in the Loess Plateau, China

by Zhifeng Jia, Hao Liu and Yan Ma

Atmosphere 2025, 16(9), 1098; https://doi.org/10.3390/atmos16091098 - 18 Sep 2025

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Abstract

Dew is an important water source for vegetation growth in arid regions and plays a significant role in maintaining ecosystem balance. The characteristics of dew formation vary under different topographic conditions. In response to the challenges posed by climate change to the sustainability [...] Read more.

Dew is an important water source for vegetation growth in arid regions and plays a significant role in maintaining ecosystem balance. The characteristics of dew formation vary under different topographic conditions. In response to the challenges posed by climate change to the sustainability of water resources and ecosystems, this study explored the impact of topography on dew formation, and leaf wetness sensors (LWSs) were employed to conduct field observations from April 2023 to April 2025 in typical hilly and gully regions of China’s Loess Plateau. We analyzed the characteristics, influencing factors, and ecological significance of near-surface water vapor condensation. The main conclusions are as follows: (1) During the observation period, dew primarily occurred between 19:00 and 07:00 the next day, peaking between 05:30 and 07:00 in the early morning. The monthly average dew amounts for the hilly region and gully region were 2.15 mm and 3.38 mm, respectively, and the monthly maximum dew amounts were 8.57 mm and 11.88 mm, respectively, both peaking in autumn, with the gully region exhibiting higher dew amounts. (2) Dew formation at a 0.2 m height was favored when relative humidity at 0.2 m exceeded 70%, the air temperature–dew point difference was less than 8 °C, the wind direction was between 150 and 210° and 240 and 270° for the hilly region and gully region, respectively, and the standardized wind speed at a 10 m height was less than 0.5 m/s and 1.5 m/s for the hilly region and gully region, respectively. (3) Moderate rainfall facilitates dew condensation. The monthly average dew-to-precipitation (dew and rain) ratio reached its maximum in November for both the Loess hilly region and gully region, at 12.88% and 18.91%, respectively. (4) The gully region experienced larger dew events more frequently than the hilly region, resulting in a higher overall dew amount in the gully region during the observation period. The dew formation characteristics observed in this study can provide a scientific basis for assessing the future supply potential of non-precipitation water sources in the Loess Plateau under climate change and their supporting role in the ecological environment. Full article

(This article belongs to the Special Issue Analysis of Dew under Different Climate Changes)

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23 pages, 2084 KB

Open AccessArticle

The Characteristics of Key Odorants from Livestock Farms and Their Mitigation Potential: A Meta-Analysis

by Yazhan Ren, Ruifang Zhang, Lu Zhang, Hongge Wang, Xinyuan Zhang, Zhaohai Bai, Lin Ma and Xuan Wang

Atmosphere 2025, 16(9), 1097; https://doi.org/10.3390/atmos16091097 - 18 Sep 2025

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Abstract

The persistent issue of odor nuisance poses significant challenges to the sustainable development of livestock farming. While previous studies have primarily focused on individual gas concentrations, a comprehensive understanding of overall odor impact based on human perception remains limited. This study introduces a [...] Read more.

The persistent issue of odor nuisance poses significant challenges to the sustainable development of livestock farming. While previous studies have primarily focused on individual gas concentrations, a comprehensive understanding of overall odor impact based on human perception remains limited. This study introduces a novel perspective by employing the odor activity value (OAV)—calculated from the ratio of gas concentration to its olfactory threshold—to evaluate the actual odor contribution of various compounds. Through a meta-analysis of data from 123 papers, we systematically assessed odor emission characteristics and mitigation strategies across different manure management stages. The results indicated that ammonia (NH₃) (with maximum concentration of 8056 ppm) and hydrogen sulfide (H₂S) (with maximum concentration of 20,057 ppm) were the most concentrated odor components in the whole manure management links. However, considering the olfactory threshold, trimethylamine (TMA) (with OAVmax 380800), H₂S (with OAVmax 48919512), butyric acid (with OAVmax 801684), and aldehydes (with OAVmax 11707) played major odor-causing roles. Notably, biological methods (83%), covering (77%), and additives (39%) were the most efficient odor mitigation strategies in the barn, manure storage, and manure treatment link, respectively. Therefore, employing the OAV-based approach is crucial for identifying priority pollutants and developing targeted control strategies across different livestock species and management stages, ultimately guiding more effective odor mitigation and healthier cohabitation. Full article

(This article belongs to the Section Air Quality and Health)

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15 pages, 7345 KB

Open AccessArticle

Increased Exposure Risk of Natural Reserves to Rainstorm in the Eastern Monsoon Region of China

by Yixuan Zhou, Hanming Cao, Lin Zhao and Shao Sun

Atmosphere 2025, 16(9), 1096; https://doi.org/10.3390/atmos16091096 - 18 Sep 2025

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Abstract

Due to climate warming, extreme precipitation events have intensified in frequency and intensity. This trend has raised significant concerns about its impact on natural reserves in eastern China’s monsoon region. A risk assessment is, therefore, needed to evaluate the vulnerability of these protected [...] Read more.

Due to climate warming, extreme precipitation events have intensified in frequency and intensity. This trend has raised significant concerns about its impact on natural reserves in eastern China’s monsoon region. A risk assessment is, therefore, needed to evaluate the vulnerability of these protected areas. Based on observed and simulated daily precipitation data, this study analyzed the spatiotemporal trends of heavy rainfall in the eastern monsoon region of China and assessed the exposure risk of the protected areas to rainstorm events both in the historical and future periods. Results indicate that the annual average number of heavy rainfall days gradually increases from northwest to southeast, displaying a distinct zonal distribution pattern. The proportion of heavy rainfall days to total precipitation days and the average intensity of heavy rainfall show peak centers in the southeastern coastal areas, western Sichuan region, and North China Plain, with minimum values observed in the northwestern direction. Protected areas in China’s Eastern Monsoon Region display a north–south gradient of precipitation exposure risk that intensifies from historical (1995–2014) to near future (2031–2050) to far future (2081–2100) under SSP245 scenario, with highest vulnerability in southeastern coastal areas. National reserves generally experience lower exposure than provincial and municipal ones, though all categories face increasing precipitation risks over time. Full article

(This article belongs to the Special Issue Climate Change and Climate Variability, and Their Impact on Extreme Events (2nd Edition))

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24 pages, 3544 KB

Open AccessArticle

A Deep Learning Model Integrating EEMD and GRU for Air Quality Index Forecasting

by Mei-Ling Huang, Netnapha Chamnisampan and Yi-Ru Ke

Atmosphere 2025, 16(9), 1095; https://doi.org/10.3390/atmos16091095 - 18 Sep 2025

Viewed by 390

Abstract

Accurate prediction of the air quality index (AQI) is essential for environmental monitoring and sustainable urban planning. With rising pollution from industrialization and urbanization, particularly from fine particulate matter (PM_2.5, PM₁₀), nitrogen dioxide (NO₂), and ozone (O [...] Read more.

Accurate prediction of the air quality index (AQI) is essential for environmental monitoring and sustainable urban planning. With rising pollution from industrialization and urbanization, particularly from fine particulate matter (PM_2.5, PM₁₀), nitrogen dioxide (NO₂), and ozone (O₃), robust forecasting tools are needed to support timely public health interventions. This study proposes a hybrid deep learning framework that combines empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) with two recurrent neural network architectures: long short-term memory (LSTM) and gated recurrent unit (GRU). A comprehensive dataset from Xitun District, Taichung City—including AQI and 18 pollutant and meteorological variables—was used to train and evaluate the models. Model performance was assessed using root mean square error, mean absolute error, mean absolute percentage error, and the coefficient of determination. Both LSTM and GRU models effectively capture the temporal patterns of air quality data, outperforming traditional methods. Among all configurations, the EEMD-GRU model delivered the highest prediction accuracy, demonstrating strong capability in modeling high-dimensional and nonlinear environmental data. Furthermore, the incorporation of decomposition techniques significantly reduced prediction error across all models. These findings highlight the effectiveness of hybrid deep learning approaches for modeling complex environmental time series. The results further demonstrate their practical value in air quality management and early-warning systems. Full article

(This article belongs to the Section Air Quality)

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12 pages, 6445 KB

Open AccessArticle

Evaporite Mineral Evidence for the Dry–Wet Variations in the Mid-Pliocene Warm Period in the Qaidam Basin

by Shun Hua, Zeng Luo, Ruipei Xie and Hansheng Wang

Atmosphere 2025, 16(9), 1094; https://doi.org/10.3390/atmos16091094 - 18 Sep 2025

Viewed by 265

Abstract

Knowledge of dry–wet variations in arid Central Asia (ACA) during the mid-Pliocene warm period (mPWP; ~3.3–3.0 Ma) is instructive to understanding the future variations in this fragile ecosystem region. However, the dry–wet variations in ACA during the mPWP remain controversial. Here, we present [...] Read more.

Knowledge of dry–wet variations in arid Central Asia (ACA) during the mid-Pliocene warm period (mPWP; ~3.3–3.0 Ma) is instructive to understanding the future variations in this fragile ecosystem region. However, the dry–wet variations in ACA during the mPWP remain controversial. Here, we present high-resolution evaporite mineralogy records from the Gansen (GS) section of the western Qaidam Basin during 3.25–2.95 Ma. Based on the similar periodic variations between the calcite content and χ_fd/HIRM value-based precipitation records, we infer that the calcite content has the potential to reflect precipitation variations. The results suggest that the calcite content reveals dominant 20 kyr precessional cycles and strong 40 kyr non-obliquity cycles, consistent with the χ_fd/HIRM values from the GS section, further demonstrating that Qaidam precipitation was affected by the intensified East Asian summer monsoon during the mPWP. However, the occurrence of gypsum beds reveals that the Qaidam Basin still experienced relatively arid climatic conditions despite the increased precipitation during this warm interval. Furthermore, halite and gypsum records suggest that the degree of aridification was relatively moderate during 3.25–3.06 Ma but intensified during 3.06–2.95 Ma. For the intensified aridification, we infer that the further global cooling, which induced a relative decrease in water vapor, played an important role at ~3.06 Ma. Taking the mPWP as the reference, our findings indicate that under continued warming the East Asian summer monsoon will bring abundant water vapor to the inland basin and alleviate aridification in ACA. However, the increased precipitation will have difficulty reversing the aridification trend in the short term. This requires us to evaluate the warming and wetting trend in ACA from a dialectical perspective. Full article

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

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22 pages, 3175 KB

Open AccessArticle

Assessing Future Heatwave-Related Mortality in Greece Using Advanced Machine Learning and Climate Projections

by Ilias Petrou, Pavlos Kassomenos and Nikolaos Kyriazis

Atmosphere 2025, 16(9), 1093; https://doi.org/10.3390/atmos16091093 - 17 Sep 2025

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Abstract

Climate change has intensified the frequency and severity of heatwaves globally, posing significant public health risks, particularly in Mediterranean countries such as Greece, where rising temperatures coincide with vulnerable aging populations. This study develops a machine learning framework employing XGBoost models to predict [...] Read more.

Climate change has intensified the frequency and severity of heatwaves globally, posing significant public health risks, particularly in Mediterranean countries such as Greece, where rising temperatures coincide with vulnerable aging populations. This study develops a machine learning framework employing XGBoost models to predict monthly heatwave-attributable mortality from cardiovascular and respiratory diseases across Greek regions, stratified by age groups. Using high-resolution climate projections under RCP4.5 and RCP8.5 scenarios, the models integrate meteorological and demographic data to capture complex nonlinear relationships and regional heterogeneity. Model performance was rigorously validated with a temporally held-out dataset, demonstrating high predictive accuracy (R² > 0.96). Projections indicate a sharp increase in elderly mortality due to heat exposure by mid-century, with marked geographic disparities emphasizing urban centers like Attica. This work advances prior studies by incorporating detailed spatial and demographic stratification and applying robust machine learning techniques beyond traditional statistical approaches. The model offers a valuable tool for public health planning and climate adaptation in Greece and similar Mediterranean contexts. Our findings highlight the urgent need for targeted mitigation strategies to address the growing burden of heatwave-related mortality under changing climate conditions. Full article

(This article belongs to the Special Issue The Drivers and Impacts of Climate Change Over the Eastern Mediterranean)

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14 pages, 4224 KB

Open AccessArticle

High-Resolution Estimation of Cropland N₂O Emissions in China Based on Machine Learning Algorithms

by Chong Liu, Zhang Wen, Jianxiao Wang and Xuejun Liu

Atmosphere 2025, 16(9), 1092; https://doi.org/10.3390/atmos16091092 - 17 Sep 2025

Viewed by 309

Abstract

Over the past two decades, agricultural nitrous oxide (N₂O) emissions have increased significantly, further intensifying their impact on global warming. Accurate emission estimates are essential for developing effective N₂O-mitigation strategies. However, the high-resolution, dynamic simulations of emissions and comprehensive [...] Read more.

Over the past two decades, agricultural nitrous oxide (N₂O) emissions have increased significantly, further intensifying their impact on global warming. Accurate emission estimates are essential for developing effective N₂O-mitigation strategies. However, the high-resolution, dynamic simulations of emissions and comprehensive analysis of their driving mechanisms in China remain unclear. In this study, we constructed a city-level agricultural N₂O emission inventory covering 336 cities in China from 2000 to 2022 based on multi-source data and machine learning algorithms. Results demonstrate that China’s cropland N₂O emissions averaged 390 Gg year⁻¹ during 2000 and 2022, exhibiting sustained growth until 2016, followed by a 13% reduction driven by the nationwide Fertilizer Reduction Policy implementation. Maize, wheat, and rice are identified as the main sources of cropland N₂O emissions. Spatially, higher N₂O emission intensities were concentrated in eastern China, and hotspots were identified in the Huang-Huai-Hai Plain (5.23 kg ha⁻¹) and the Middle-Lower Yangtze River Plain (2.95 kg ha⁻¹). These emission patterns are primarily influenced by soil organic carbon, crop type, and fertilizer-management practices. This study provides robust data support and methodological basis for formulating agricultural mitigation policies. Full article

(This article belongs to the Special Issue Advanced Research on Anthropogenic Pollutant Emission Inventory)

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25 pages, 1221 KB

Open AccessArticle

Simulations of Drainage Flows with Topographic Shading and Surface Physics Inform Analytical Models

by Alex Connolly and Fotini Katopodes Chow

Atmosphere 2025, 16(9), 1091; https://doi.org/10.3390/atmos16091091 - 17 Sep 2025

Viewed by 192

Abstract

We perform large-eddy simulations (LESs) with realistic radiation, including topographic shading, and an advanced land surface model to investigate drainage flow dynamics in an idealized compound-slope mountain geometry. This allows an analysis not only of fully developed profiles in steady state—the subject of [...] Read more.

We perform large-eddy simulations (LESs) with realistic radiation, including topographic shading, and an advanced land surface model to investigate drainage flow dynamics in an idealized compound-slope mountain geometry. This allows an analysis not only of fully developed profiles in steady state—the subject of existing analytical solutions—but also of transient two- and three-dimensional dynamics. The evening onset of downslope flow is related to the duration of shadow front propagation along the eastern slopes, for which an analytic form is derived. We demonstrate that the flow response to this radiation pattern is mediated by the thermal inertia of the land through sensitivity to soil moisture. Onset timing differences on opposite sides of the peak are explained by convective structures that persist after sunset over the western slopes when topographic shading is considered. Although these preceding convective systems, as well as the presence of neighboring terrain, inhibit the initial development of drainage flows, the LES develops an approximately steady-state, fully developed flow over the finite slopes and finite nocturnal period. This allows a comparison to analytical models restricted to such cases. New analytical solutions based on surface heat flux boundary conditions, which can be estimated by the coupled land surface model, suggest the need for improved representation of the eddy diffusivity for analytical models of drainage flows. Full article

(This article belongs to the Special Issue Atmospheric Boundary Layer Processes, Characteristics and Parameterization (3rd Edition))

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24 pages, 6155 KB

Open AccessReview

Keyword Analysis and Systematic Review of China’s Sponge City Policy and Flood Management Research

by Yichen Lu, Muge Huang, Haixin Xiao, Zekun Lu, Mingjing Xie and Kaida Chen

Atmosphere 2025, 16(9), 1090; https://doi.org/10.3390/atmos16091090 - 16 Sep 2025

Viewed by 414

Abstract

With the acceleration of climate change and urbanisation, Chinese cities are facing increasingly severe flood risks. To address this challenge, China began implementing its sponge city policy in 2013, leveraging low-impact development, green infrastructure construction, and integrated water resource management to enhance urban [...] Read more.

With the acceleration of climate change and urbanisation, Chinese cities are facing increasingly severe flood risks. To address this challenge, China began implementing its sponge city policy in 2013, leveraging low-impact development, green infrastructure construction, and integrated water resource management to enhance urban resilience to floods and improve water security. This study utilises the Web of Science database as a reference, retrieving 201 relevant literature sources. From these, 61 studies closely related to China’s sponge city policy and urban flood management were selected. CiteSpace was employed to conduct keyword co-occurrence and temporal evolution analyses, comprehensively outlining the research hotspots and developmental trajectory of this field. The results indicate that research content has gradually shifted from early engineering-based flood control models to multi-objective, interdisciplinary comprehensive management, encompassing flood risk assessment, policy implementation mechanisms, integration of green infrastructure, and economic feasibility analysis. Based on this, this paper constructs an analytical framework incorporating technical, environmental, institutional, and social dimensions to integrate existing research findings, while identifying gaps in cross-scale coordination, smart management, and public participation. The research conclusions can provide valuable references for future policy optimisation and urban sustainable development. Full article

(This article belongs to the Section Meteorology)

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16 pages, 1620 KB

Open AccessArticle

Assessment of Radiological Plume Dispersion in LBLOCA-Type Accidents at Nuclear Power Plants

by Juliana de Sá Sanchez Machado, Diego José Silva Nuzza de Souza, Maria Lurdes Dinis and Andressa dos Santos Nicolau

Atmosphere 2025, 16(9), 1089; https://doi.org/10.3390/atmos16091089 - 16 Sep 2025

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Abstract

This study analyzed the radiation dose rate in air, water and soil following a simulated Large Break LOCA (LBLOCA) accident in a Pressurized Water Reactor (PWR) nuclear power plant with a point-source release of radionuclides into the atmosphere. AERMOD and RESRAD-BIOTA 1.8 codes [...] Read more.

This study analyzed the radiation dose rate in air, water and soil following a simulated Large Break LOCA (LBLOCA) accident in a Pressurized Water Reactor (PWR) nuclear power plant with a point-source release of radionuclides into the atmosphere. AERMOD and RESRAD-BIOTA 1.8 codes were used, with meteorological data processed by AERMET and terrain elevation data generated using AERMAP. AERMOD performed dispersion calculations using Gaussian and bi-Gaussian models. The simulations identified atmospheric stability classes C and F, which, combined with other external factors, directly influenced the dose rates and the distances reached by the radioactive plume. The dose rate analysis, based on calculated concentrations in the air, water and soil, indicated that, in this scenario, the potential release of radioactive material does not pose a threat to the population. The adopted methodology proved effective in mapping the behavior of the radioactive plume across the three media, providing accurate and reliable results for use in safety assessments and emergency response planning. Full article

(This article belongs to the Section Air Quality)

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20 pages, 3437 KB

Open AccessArticle

Semi-Quantitative Characterization of Volatile Organic Compounds in Indoor and Outdoor Air Using Passive Samplers: A Case Study of Milan, Italy

by Vllaznim Mula, Jane Bogdanov, Jasmina Petreska Stanoeva, Lulzim Zeneli, Valbonë Mehmeti, Fabrizio Gelmini, Armond Daci, Avni Berisha, Zoran Zdravkovski and Giangiacomo Beretta

Atmosphere 2025, 16(9), 1088; https://doi.org/10.3390/atmos16091088 - 16 Sep 2025

Cited by 1 | Viewed by 834

Abstract

This study presents a semi-quantitative characterization of volatile organic compound (VOC) concentrations and their emission sources in indoor and outdoor environments across four residential and laboratory sites in Milan, Italy, during the summer of 2024. Radiello^® passive samplers (Fondazione Salvatore Maugeri in [...] Read more.

This study presents a semi-quantitative characterization of volatile organic compound (VOC) concentrations and their emission sources in indoor and outdoor environments across four residential and laboratory sites in Milan, Italy, during the summer of 2024. Radiello^® passive samplers (Fondazione Salvatore Maugeri in Padova, Italy) were employed for VOC collection, followed by gas chromatography–mass spectrometry analysis. The semi-quantitative mean total VOC (TVOC) concentration was 220.8 ± 195.4 µg/m³ for the outdoor air and slightly higher at 243.6 ± 134.3 µg/m³ for the indoor air, resulting in an indoor-to-outdoor relative ratio of 1.10. The outdoor VOC profile was dominated by hydrocarbons, accounting for 80.3% ± 4.6% (173.2 ± 143.8 µg/m³) of TVOCs, followed by aromatic hydrocarbons at 13.3% ± 5.5% (37.2 ± 49.7 µg/m³). Indoors, hydrocarbons also predominated, representing 34.1% ± 15.2% (95.2 ± 80.1 µg/m³) of the TVOCs, followed by terpenes at 20.7% ± 15.5% (49.0 ± 46.4 µg/m³). Other VOC groups contributed smaller fractions in both environments. The emission profiles from cleaning and personal care products were assessed semi-quantitatively to determine their relative percentage contributions to the indoor VOCs. Source attribution was further supported by diagnostic relative ratios—benzene/toluene, toluene/benzene, and (m + p)-xylene/ethylbenzene—which provided insight into dominant emission sources and photochemical aging. Full article

(This article belongs to the Section Air Quality)

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23 pages, 2122 KB

Open AccessReview

The Rectification of ENSO into the Mean State: A Review of Theory, Mechanisms, and Implications

by Jin Liang, Nan Zhou, De-Zheng Sun and Wei Liu

Atmosphere 2025, 16(9), 1087; https://doi.org/10.3390/atmos16091087 - 15 Sep 2025

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Abstract

The El Niño–Southern Oscillation (ENSO) is the most consequential mode of interannual climate variability on the planet, yet its prediction has become complex due to the inability of classical paradigms to explain the observed co-evolution of the tropical mean state and interannual variability [...] Read more.

The El Niño–Southern Oscillation (ENSO) is the most consequential mode of interannual climate variability on the planet, yet its prediction has become complex due to the inability of classical paradigms to explain the observed co-evolution of the tropical mean state and interannual variability on decadal timescales. This article synthesizes the extensive research on ENSO rectification, exploring a paradigm that resolves this causality problem by recasting ENSO as an active architect of its own mean state. Tracing the intellectual development of this theory, starting from fundamental concepts such as the “dynamical thermostat” and “heat pump” hypotheses, modern analysis has identified the core physical mechanism as nonlinear dynamical heating (NDH), which is rooted in nonlinear heat advection during asymmetric ENSO cycles. The convergence of evidence from forced ocean models and observational diagnostics confirms a rectified signal characterized by an off-equatorial spatial pattern, providing a primary mechanism for tropical Pacific decadal variability (TPDV). By establishing a coherent framework linking high-frequency asymmetry with low-frequency variations, this review lays the foundation for future research and emphasizes the critical role of the rectification effect in improving decadal climate prediction. 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)

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26 pages, 28301 KB

Open AccessArticle

Small but Notable Influence of Numerical Diffusion on Super Coarse Dust Sedimentation: Insights from UNO3 vs. Upwind Schemes

by Eleni Drakaki, Sotirios Mallios, Carlos Perez García-Pando, Petros Katsafados and Vassilis Amiridis

Atmosphere 2025, 16(9), 1086; https://doi.org/10.3390/atmos16091086 - 15 Sep 2025

Viewed by 246

Abstract

Mineral dust plays a vital role in the Earth’s climate system, influencing radiation, cloud formation, biogeochemical cycles, and air quality. Accurately simulating dust transport in atmospheric models remains challenging, particularly for coarse and super-coarse particles, which are often underrepresented due to limitations in [...] Read more.

Mineral dust plays a vital role in the Earth’s climate system, influencing radiation, cloud formation, biogeochemical cycles, and air quality. Accurately simulating dust transport in atmospheric models remains challenging, particularly for coarse and super-coarse particles, which are often underrepresented due to limitations in model physics and numerical treatment. Observations have shown that particles larger than 20 μm can remain airborne longer than expected, suggesting that standard gravitational settling formulations may be insufficient. One potential contributor to this discrepancy is the numerical diffusion introduced by advection schemes used to model sedimentation processes. In this study, we compare the commonly used first-order upwind advection scheme, which is highly diffusive, to a third-order scheme (UNO3) that reduces numerical diffusion while maintaining computational efficiency. Using 2-D sensitivity tests, we show that UNO3 retains up to 50% more dust mass for the coarsest particles compared to the default scheme, although overall dust lifetime shows little change. In 3-D simulations of the ASKOS 2022 dust campaign, both schemes reproduced similar large-scale dust patterns, with UNO3 yielding slightly lower dust. Overall, domain-averaged dust load differences remain small (less than 2%), with minor decreases in fine dust ~3% and slight increases in coarse dust ~2%, indicating that reducing numerical diffusion modestly enhances the presence of larger particles. Near the surface, UNO3 produces a ~4% increase in dust concentration, with local differences up to 50 μg/m³. These results highlight that while numerical diffusion does affect dust transport—especially for super-coarse fractions—its impact is relatively small compared to the larger underestimation of super-coarse dust commonly observed in models compared to measurements. Addressing the fundamental physics of super-coarse dust emission and lofting may therefore be a higher priority for improving dust model fidelity than further refining advection numerics. Future studies may also consider implementing more computationally intensive schemes, such as the Prather scheme, to further minimize numerical diffusion where highly accurate size-resolved transport is critical. Full article

(This article belongs to the Section Aerosols)

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15 pages, 4404 KB

Open AccessArticle

Spatiotemporal Distribution of Lightning-Caused Wildfires on Mount Mainalo, Central Peloponnese, Greece

by Miltiadis Athanasiou, Athanasios Karadimitris, Ioannis Kouretas and Panagiotis Nastos

Atmosphere 2025, 16(9), 1085; https://doi.org/10.3390/atmos16091085 - 15 Sep 2025

Viewed by 412

Abstract

This paper presents findings based on eighty (80) lightning-caused wildfires that occurred on Mount Mainalo, in central Peloponnese, Greece, from May 1998 to November 2022. The frequency of lightning-caused wildfires was found to increase in July and August, consistent with the occurrence of [...] Read more.

This paper presents findings based on eighty (80) lightning-caused wildfires that occurred on Mount Mainalo, in central Peloponnese, Greece, from May 1998 to November 2022. The frequency of lightning-caused wildfires was found to increase in July and August, consistent with the occurrence of dry summer thunderstorms. Most wildfires ignited in the southern part of the mountain, at elevations between 1200 and 1800 m, and were primarily detected in the afternoon hours. We present spatial data, statistics and frequency distribution histograms of subsets of the database. The likelihood of at least one fire per season is approximately 96%, while the average number of wildfires per fire season is 3.2. These findings on the number of lightning-caused wildfires per year, the holdover time (the time interval between the ignition and fire detection), the wildfire detection time, the elevation of lightning-caused wildfire occurrence, the total annual burned area and the burned area per fire can support improving wildfire management in the region since they provide a thorough description of the regime of lightning-caused wildfire on Mount Mainalo. This research addresses a critical knowledge gap in the study of lightning-caused wildfires in the Mediterranean, which remain underexplored despite their growing relevance under climate change. Full article

(This article belongs to the Special Issue Climate and Weather Extremes in the Mediterranean)

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36 pages, 12116 KB

Open AccessArticle

Deciphering Air Pollution Dynamics and Drivers in Riverine Megacities Using Remote Sensing Coupled with Geospatial Analytics for Sustainable Development

by Almustafa Abd Elkader Ayek, Mohannad Ali Loho, Wafa Saleh Alkhuraiji, Safieh Eid, Mahmoud E. Abd-Elmaboud, Faten Nahas and Youssef M. Youssef

Atmosphere 2025, 16(9), 1084; https://doi.org/10.3390/atmos16091084 - 15 Sep 2025

Viewed by 815

Abstract

Air pollution represents a critical environmental challenge in stressed riverine cities, particularly in regions experiencing rapid urbanization and inadequate emission management infrastructure. This study investigates the spatio-temporal dynamics of atmospheric pollution in Baghdad, Iraq, during 2012–2023, analyzing seven key pollutants (CO, CO₂ [...] Read more.

Air pollution represents a critical environmental challenge in stressed riverine cities, particularly in regions experiencing rapid urbanization and inadequate emission management infrastructure. This study investigates the spatio-temporal dynamics of atmospheric pollution in Baghdad, Iraq, during 2012–2023, analyzing seven key pollutants (CO, CO₂, SO₂, SO₄, O₃, CH₄, and AOD) using NASA’s Giovanni platform coupled with Google Earth Engine analytics. Monthly time-series data were processed through advanced statistical techniques, including Seasonal Autoregressive Integrated Moving Average (SARIMA) modeling and correlation analysis with meteorological parameters, to identify temporal trends, seasonal variations, and driving mechanisms. The analysis revealed three distinct pollutant trajectory categories reflecting complex emission–atmosphere interactions. Carbon monoxide exhibited dramatic decline (60–70% reduction from 2021), attributed to COVID-19 pandemic restrictions and demonstrating rapid responsiveness to activity modifications. Conversely, greenhouse gases showed persistent accumulation, with CO₂ increasing from 400.5 to 417.5 ppm and CH₄ rising 5.9% over the study period, indicating insufficient mitigation efforts. Sulfur compounds and ozone displayed stable concentrations with pronounced seasonal oscillations (winter peaks 2–3 times summer levels), while aerosol optical depth showed high temporal variability linked to dust storm events. Spatial analysis identified pronounced urban–rural concentration gradients, with central Baghdad CO levels exceeding 0.40 ppm compared to peripheral regions below 0.20 ppm. Linear concentration patterns along transportation corridors and industrial zones confirmed anthropogenic source dominance. Correlation analysis revealed strong relationships between meteorological factors and pollutant concentrations (atmospheric pressure: r = 0.62–0.70 with NO₂), providing insights for integrated climate–air quality management strategies. The study demonstrates substantial contributions to Sustainable Development Goals across four dimensions (Environmental Health 30%, Sustainable Cities and Climate Action 25%, Economic Development 25%, and Institutional Development 20%) while providing transferable methodological frameworks for evidence-based policy interventions and environmental monitoring in similar stressed urban environments globally. Full article

(This article belongs to the Special Issue Remote Sensing and GIS Technology in Atmospheric Research)

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17 pages, 3422 KB

Open AccessArticle

Impact of Spatial Resolution on River Flow Simulation Based on the Total Runoff Integrating Pathway (TRIP) Model

by Minwoo Kim, Ui-Yong Byun, Eun-Chul Chang and Yoon-Jin Lim

Atmosphere 2025, 16(9), 1083; https://doi.org/10.3390/atmos16091083 - 15 Sep 2025

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Abstract

Although the impact of spatial resolution on river flow simulation has been examined in several studies, unresolved uncertainties remain regarding parameter sensitivity and the applicability of different routing models. This study investigated the resolution dependency of the total runoff integrating pathway (TRIP) river [...] Read more.

Although the impact of spatial resolution on river flow simulation has been examined in several studies, unresolved uncertainties remain regarding parameter sensitivity and the applicability of different routing models. This study investigated the resolution dependency of the total runoff integrating pathway (TRIP) river routing model while focusing on East Asia. With the increasing spatial resolution of Earth system models (ESMs), understanding the effects of resolution changes on river discharge characteristics is essential for conducting accurate hydrological simulations. In this study, we conducted sensitivity experiments using the TRIP model at resolutions of 0.5°, 1°, and 0.125° while considering idealized and real-case scenarios. The results indicate significant improvements in the representation of river networks and discharge dynamics at higher resolutions, highlighting the need for parameter adjustments, particularly with respect to flow velocity and meandering factors. Parameters were optimized based on matching the travel time of runoff from precipitation sources to river mouths. The optimized parameters yielded consistent river storage and discharge results across different resolutions, enhancing the reliability of high-resolution hydrological modeling. Our study highlights the importance of resolution-aware modeling in improving the simulations of hydrological processes in different climate systems. Notably, our study can serve as a foundation for future interdisciplinary studies on climate modeling, river discharge and flow simulations, and hydrogeology. Full article

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

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Atmosphere, Volume 16, Issue 9 (September 2025) – 118 articles

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