Atmosphere

13 pages, 934 KiB

Open AccessArticle

Performance Evaluation of PM_2.5 Forecasting Using SARIMAX and LSTM in the Korean Peninsula

by Chae-Yeon Lee, Ju-Yong Lee, Seung-Hee Han, Jin-Goo Kang, Jeong-Beom Lee and Dae-Ryun Choi

Atmosphere 2025, 16(5), 524; https://doi.org/10.3390/atmos16050524 (registering DOI) - 29 Apr 2025

Air pollution, particularly fine particulate matter (PM_2.5), poses significant environmental and public health challenges in South Korea. The National Institute of Environmental Research (NIER) currently relies on numerical models such as the Community Multiscale Air Quality (CMAQ) model for PM_2.5 [...] Read more.

Air pollution, particularly fine particulate matter (PM_2.5), poses significant environmental and public health challenges in South Korea. The National Institute of Environmental Research (NIER) currently relies on numerical models such as the Community Multiscale Air Quality (CMAQ) model for PM_2.5 forecasting. However, these models exhibit inherent uncertainties due to limitations in emission inventories, meteorological inputs, and model frameworks. To address these challenges, this study evaluates and compares the forecasting performance of two alternative models: Long Short-Term Memory (LSTM), a deep learning model, and Seasonal Auto Regressive Integrated Moving Average with Exogenous Variables (SARIMAX), a statistical model. The performance evaluation was focused on Seoul, South Korea, and took place over different forecast lead times (D00–D02). The results indicate that for short-term forecasts (D00), SARIMAX outperformed LSTM in all statistical metrics, particularly in detecting high PM_2.5 concentrations, with a 19.43% higher Probability of Detection (POD). However, SARIMAX exhibited a sharp performance decline in extended forecasts (D01–D02). In contrast, LSTM demonstrated relatively stable accuracy over longer lead times, effectively capturing complex PM_2.5 concentration patterns, particularly during high-concentration episodes. These findings highlight the strengths and limitations of statistical and deep learning models. While SARIMAX excels in short-term forecasting with limited training data, LSTM proves advantageous for long-term forecasting, benefiting from its ability to learn complex temporal patterns from historical data. The results suggest that an integrated air quality forecasting system combining numerical, statistical, and machine learning approaches could enhance PM_2.5 forecasting accuracy. Full article

(This article belongs to the Special Issue Novel Insights into Air Pollution over East Asia (Second Edition))

26 pages, 861 KiB

Open AccessArticle

Integration of Explainable Artificial Intelligence into Hybrid Long Short-Term Memory and Adaptive Kalman Filter for Sulfur Dioxide (SO₂) Prediction in Kimberley, South Africa

by Israel Edem Agbehadji and Ibidun Christiana Obagbuwa

Atmosphere 2025, 16(5), 523; https://doi.org/10.3390/atmos16050523 (registering DOI) - 29 Apr 2025

Abstract

Air pollution remains one of the environmental issues affecting some countries, which leads to health issues globally. Though several machine learning and deep learning models are used to analyze air pollutants, model interpretability is a challenge. Also, the dynamic and time-varying nature of [...] Read more.

Air pollution remains one of the environmental issues affecting some countries, which leads to health issues globally. Though several machine learning and deep learning models are used to analyze air pollutants, model interpretability is a challenge. Also, the dynamic and time-varying nature of air pollutants often creates noise in measurements, making air pollutant prediction (e.g., Sulfur Dioxide (SO₂) concentration) inaccurate, which influences a model’s performance. Recent advancements in artificial intelligence (AI), particularly explainable AI, offer transparency and trust in the deep learning models. In this regard, organizations using traditional machine and deep learning models are confronted with how to integrate explainable AI into air pollutant prediction systems. In this paper, we propose a novel approach that integrates explainable AI (xAI) into long short-term memory (LSTM) models and attempts to address the noise by Adaptive Kalman Filters (AKFs) and also includes causal inference analysis. By utilizing the LSTM, the long-term dependencies in daily air pollutant concentration and meteorological datasets (between 2008 and 2024) for the City of Kimberley, South Africa, are captured and analyzed in multi-time steps. The proposed model (AKF_LSTM_xAI) was compared with LSTM, the Gate Recurrent Unit (GRU), and LSTM-multilayer perceptron (LSTM-MLP) at different time steps. The performance evaluation results based on the root mean square error (RMSE) for the one-day time step suggest that AKF_LSTM_xAI guaranteed 0.382, LSTM (2.122), LSTM_MLP (3.602), and GRU (2.309). The SHapley Additive exPlanations (SHAP) value reveals “Relative_humidity_t0” as the most influential variable in predicting the SO₂ concentration, whereas LIME values suggest that high “wind_speed_t0” reduces the predicted SO₂ concentration. Full article

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

20 pages, 21451 KiB

Open AccessArticle

Automatic Detection of Whistler Waves in the Top-Side Ionosphere: The WhISPER Technique

by Dario Recchiuti, Roberto Battiston, Giulia D’Angelo, Emanuele Papini, Coralie Neubüser, William Jerome Burger and Mirko Piersanti

Atmosphere 2025, 16(5), 522; https://doi.org/10.3390/atmos16050522 (registering DOI) - 29 Apr 2025

Abstract

We introduce the Whistler Identification by Spectral Power Estimation and Recognition (WhISPER) algorithm, a novel automated technique for detecting whistler waves in the top side of the Earth’s ionosphere. WhISPER is the first step towards a comprehensive system designed to accumulate and analyze [...] Read more.

We introduce the Whistler Identification by Spectral Power Estimation and Recognition (WhISPER) algorithm, a novel automated technique for detecting whistler waves in the top side of the Earth’s ionosphere. WhISPER is the first step towards a comprehensive system designed to accumulate and analyze a large dataset of whistler observations, which has been developed to advance our understanding of whistler generation and propagation. Unlike conventional image-correlation-based techniques, WhISPER identifies whistlers based on their energy content, enhancing computational efficiency. This work presents the results of applying WhISPER to four years (2019–2022) of top-side ionospheric magnetic field data. A statistical analysis of over 800,000 detected whistlers reveals a strong correlation with lightning activity and (as expected) higher occurrence rates during local summer months. The presented results demonstrate the excellent performance of the WhISPER technique in identifying whistler events. Full article

(This article belongs to the Special Issue Recent Advances in Ionosphere Observation and Investigation (2nd Edition))

16 pages, 2935 KiB

Open AccessArticle

Analysis of Fine Dust Impacts on Incheon and Busan Port Areas Resulting from Port Emission Reduction Measures

by Moon-Seok Kang, Jee-Ho Kim, Young Sunwoo and Ki-Ho Hong

Atmosphere 2025, 16(5), 521; https://doi.org/10.3390/atmos16050521 (registering DOI) - 29 Apr 2025

Abstract

PM_2.5 concentrations in major port cities in the Republic of Korea, such as Incheon and Busan, are as serious as those in land-based metropolises, such as Seoul, and fine dust generated in port cities is mainly emitted from ships. To identify the [...] Read more.

PM_2.5 concentrations in major port cities in the Republic of Korea, such as Incheon and Busan, are as serious as those in land-based metropolises, such as Seoul, and fine dust generated in port cities is mainly emitted from ships. To identify the specific substances influencing local air quality, the occurrence and effects of high concentrations of PM_2.5 at the ports of Incheon and Busan were analyzed. To analyze the effects of improving air quality based on the Republic of Korea’s port and ship-related reduction measures, we calculated an emissions forecast for 2025 following the implementation/non-implementation of these measures and analyzed all impacts using the WRF-SMOKE-CMAQ modeling system. The ratio of ionic components constituting PM_2.5 at the ports of Incheon and Busan was generally high in nitrate composition; however, the ratio of sulfate was high on high PM_2.5 concentration days. When implementing measures to reduce emissions related to ports and ships, forecasted PM_2.5 and SO₂ emissions showed substantial decreases in port areas as well as nearby land and sea areas. The associated decrease in the PM_2.5 concentration was highly influential in reducing the concentration of sulfate. Full article

(This article belongs to the Special Issue Atmospheric Pollution in Highly Polluted Areas)

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27 pages, 5861 KiB

Open AccessArticle

Analysis and Trends of the Stability Indices During Hail Days Derived from the Radiosonde Observations from Belgrade (Serbia)

by Dragana Vujović, Vladan Vučković and Aleksandar Zečević

Atmosphere 2025, 16(5), 520; https://doi.org/10.3390/atmos16050520 (registering DOI) - 29 Apr 2025

Abstract

Forecasting thunderstorms, along with their intensity and phenomenon, is still one of the most challenging tasks in modern weather forecasting. One of the methods for this prediction is based on the indices of convective instability in the atmosphere. For the first time, we [...] Read more.

Forecasting thunderstorms, along with their intensity and phenomenon, is still one of the most challenging tasks in modern weather forecasting. One of the methods for this prediction is based on the indices of convective instability in the atmosphere. For the first time, we analysed the values and trends of 23 stability indices on days when hail occurred. From 2005 to 2020, the most frequently observed hailstones had a diameter between 13 and 20 mm, which accounted for 35.8% of all hail days, which was 826. Huge hailstones with a greater than 50 mm diameter were observed on only two days. Eight of the 23 stability indices show a monotonically decreasing (Showalter Index, Lifted Index, Lifted Index using the virtual temperature, and Humidity Index) or increasing trend (K Index, Convective Available Potential Energy for the most unstable air parcel and for mixing layer, and Convective Available Potential Energy in the layer between air temperatures −10 and −30 °C). These trends indicate that the environment is becoming increasingly favourable for the formation of thunderstorms. However, this potential does not appear to be fully realised, as the frequency of severe and large hail (with diameters of 21 mm or more) has not increased during the period studied. Full article

(This article belongs to the Section Meteorology)

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21 pages, 3086 KiB

Open AccessArticle

Measuring Ammonia Concentration Distributions with Passive Samplers to Evaluate the Impact of Vehicle Exhaust on a Roadside Environment in Tokyo, Japan

by Hiroyuki Hagino

Atmosphere 2025, 16(5), 519; https://doi.org/10.3390/atmos16050519 (registering DOI) - 29 Apr 2025

Abstract

Evaluating the impact on roadside environments of NH₃ from vehicle emissions is important for protecting the ecosystem from air pollution by fine particulate matter and nitrogen deposition. This study used passive samplers to measure NH₃ and NO_X at multiple points [...] Read more.

Evaluating the impact on roadside environments of NH₃ from vehicle emissions is important for protecting the ecosystem from air pollution by fine particulate matter and nitrogen deposition. This study used passive samplers to measure NH₃ and NO_X at multiple points near a major road to observe the distribution of these gases in the area. The impact of NH₃ emitted from vehicles on a major road on the environmental concentration of NH₃ at different distances from the roadside was found to be similar to that of NO_X and NO₂. The concentration of NH₃ rapidly decreased due to dilution and diffusion within approximately 50 m of the road, and after 100 m the concentration remained almost the same or decreased slowly. Furthermore, CO₂ observations taken in the same period along the roadside and in the background yielded a vehicular emission factor of 4–50 mg/km for NH₃, which is comparable with previous research. This emission factor level contributes 4–11 ppb to the NH₃ concentrations in roadside air through the dilution and diffusion process. A correlation was found between the emission factors of NH₃ and NO_X that was different from the trade-off relationship seen when single-vehicle exhaust is measured. Full article

(This article belongs to the Special Issue Ammonia Emissions and Particulate Matter (2nd Edition))

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16 pages, 1763 KiB

Open AccessArticle

Unveiling Cloud Microphysics of Marine Cold Air Outbreaks Through A-Train’s Active Instrumentation

by Kamil Mroz, Ranvir Dhillon and Alessandro Battaglia

Atmosphere 2025, 16(5), 518; https://doi.org/10.3390/atmos16050518 (registering DOI) - 28 Apr 2025

Viewed by 14

Abstract

Marine Cold Air Outbreaks (MCAOs) are critical drivers of high-latitude climates because they regulate the exchange of heat, moisture, and momentum between cold continental or polar air masses and relatively warmer ocean surfaces. In this study, we combined CloudSat–CALIPSO observations (2007–2017) with ERA5 [...] Read more.

Marine Cold Air Outbreaks (MCAOs) are critical drivers of high-latitude climates because they regulate the exchange of heat, moisture, and momentum between cold continental or polar air masses and relatively warmer ocean surfaces. In this study, we combined CloudSat–CALIPSO observations (2007–2017) with ERA5 reanalysis data to investigate the microphysical properties and vertical structure of snowfall during MCAOs. By classifying events using a low-level instability parameter, we provide a detailed comparison of the vertical and spatial characteristics of different snowfall regimes, focusing on key cloud properties such as the effective radius, particle concentration, and ice water content. Our analysis identified two distinct snowfall regimes: shallow stratocumulus-dominated snowfall, prevalent during typical MCAOs and characterized by cloud top heights below 3 km and a comparatively lower ice water content (IWC), and deeper snowfall occurring during non-CAO conditions. We demonstrate that, despite their lower instantaneous snowfall rates, CAO-related snowfall events cumulatively contribute significantly to the total ice mass production in the subpolar North Atlantic. Additionally, CAO events are characterized by a greater number of ice particles near the surface, which are also smaller (

r_{eff}

of 59 μm versus 62 μm) than those associated with non-CAO events. These microphysical differences impact cloud optical properties, influencing the surface radiative balance. Full article

(This article belongs to the Section Meteorology)

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20 pages, 1954 KiB

Open AccessArticle

Analysis of Nitrogen Dioxide Concentration at Highway Toll Stations Based on fsQCA—Data Sourced from Sentinel-5P

by Shenghao Xu and Xinxiang Yang

Atmosphere 2025, 16(5), 517; https://doi.org/10.3390/atmos16050517 - 28 Apr 2025

Viewed by 26

Abstract

The Fuzzy-Set Qualitative Comparative Analysis (fsQCA) method is employed in this study to investigate the combined effects of region area, the number of COVID-19 infections, and the number of family cars on NO₂ concentration at key highway toll stations in Zhejiang Province, [...] Read more.

The Fuzzy-Set Qualitative Comparative Analysis (fsQCA) method is employed in this study to investigate the combined effects of region area, the number of COVID-19 infections, and the number of family cars on NO₂ concentration at key highway toll stations in Zhejiang Province, China. By selecting and comparing typical cases, the analysis reveals differentiated characteristics in how various factor combinations influence NO₂ concentration. The main findings are as follows: Under COVID-19 lockdown measures, prolonged vehicle waiting times and a shift towards family car usage among the public have led to a significant increase in NO₂ concentration at highway toll stations. Promoting the Electronic Toll Collection (ETC) system and encouraging public transportation are of great importance. The synergistic effects of COVID-19 lockdown policies and urban land area, resulting in the reduction in the number of family cars and the excellent air circulation conditions in large cities, have contributed to the decrease in NO₂ concentration at highway toll stations. Increasing urban green spaces and promoting clean energy vehicles are crucial for advancing urban sustainable development. The combined analysis of the region area and the number of family cars indicates that a higher proportion of large vehicles contributes to improving transportation efficiency, but also results in elevated NO₂ concentration at highway toll stations due to diesel emissions. Optimizing the transportation structure and reducing reliance on large vehicles are of significant importance. Full article

(This article belongs to the Special Issue Recent Advances in Mobile Source Emissions (2nd Edition))

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17 pages, 4153 KiB

Open AccessArticle

Cluster Analysis and Atmospheric Circulation Features of Springtime Compound Dry-Hot Events in the Pearl River Basin

by Ruixin Duan, Feng Wang, Jiannan Zhang and Xiong Zhou

Atmosphere 2025, 16(5), 516; https://doi.org/10.3390/atmos16050516 - 28 Apr 2025

Viewed by 36

Abstract

Compound dry–hot events refer to climate phenomena where drought and high temperatures occur simultaneously. Compared to single extreme events, compound dry–hot events may have greater adverse impacts. This study uses high-spatial-resolution observational data (i.e., temperature, precipitation, and climate water balance) to cluster and [...] Read more.

Compound dry–hot events refer to climate phenomena where drought and high temperatures occur simultaneously. Compared to single extreme events, compound dry–hot events may have greater adverse impacts. This study uses high-spatial-resolution observational data (i.e., temperature, precipitation, and climate water balance) to cluster and identify spring compound dry–hot events in the Pearl River Basin over the past nearly 50 years. It further investigates the associated large-scale atmospheric circulation conditions during compound dry–hot events. Using three clustering methods and twenty-six evaluation criteria, six events are identified. These events primarily exhibit negative anomalies in precipitation and climate water balance and positive anomalies in temperature. The spatial distribution results show that moisture deficits during compound events are mainly concentrated in the eastern Pearl River Basin, especially in the Pearl River Delta region. An atmospheric circulation analysis indicates that spring compound dry–hot events in the Pearl River Basin are commonly accompanied by persistent abnormal high-pressure systems, relatively weak westerly transport from subtropical regions such as the Indian Ocean and the Bay of Bengal (20–25 °N), and limited moisture input from the western Pacific region. The results of this study can help to better understand and analyze the risk changes of extreme events in the context of global warming. Full article

(This article belongs to the Special Issue Advances in Understanding Extreme Weather Events in the Anthropocene)

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24 pages, 1685 KiB

Open AccessReview

Characteristics, Distribution, and Sources of Atmospheric Microplastics in Southeast Asia: A Scoping Review

by Nur Nabila Abd Rahim, Patrick Wee Yao Peng, Nurul Farehah Shahrir, Wan Rozita Wan Mahiyuddin, Sharifah Mazrah Sayed Mohamed Zain and Rohaida Ismail

Atmosphere 2025, 16(5), 515; https://doi.org/10.3390/atmos16050515 - 28 Apr 2025

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Abstract

This scoping review examines the distribution, sources, and characterization of atmospheric microplastics (AMPs) in Southeast Asia (SEA), following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. A comprehensive search of Scopus and PubMed identified 58 relevant [...] Read more.

This scoping review examines the distribution, sources, and characterization of atmospheric microplastics (AMPs) in Southeast Asia (SEA), following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. A comprehensive search of Scopus and PubMed identified 58 relevant articles, with 16 meeting the inclusion criteria. Findings indicate high microplastic (MP) concentrations in urban centres, notably in Malaysia, Indonesia, and Thailand, a pattern driven by rapid urbanisation, industrial emissions, textile production, and insufficient waste management. Predominant polymer types include polyethylene (PE), polypropylene (PP), and polyester (PET), with fibres and black particles being the most common forms. Black particles, often linked to tire wear and vehicular emissions, underscore traffic pollution’s role in AMP distribution, while PET fibres reflect the influence of SEA’s textile industry. Geographic gaps were observed, with limited studies in countries such as Cambodia and Laos. The review highlights the need for standardised sampling and quantification methods to ensure data comparability and calls for expanded research into rural and coastal regions. Future studies should prioritise longitudinal investigations into the effects of chronic exposure on health; this is particularly relevant for nanoplastics (NPs) because of their greater potential for biological penetration. These insights form a crucial foundation for mitigating AMP pollution in SEA. Full article

(This article belongs to the Special Issue Toxicity of Persistent Organic Pollutants and Microplastics in Air)

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23 pages, 7371 KiB

Open AccessArticle

Red-Hot Portugal: Mapping the Increasing Severity of Exceptional Maximum Temperature Events (1980–2024)

by Luis Angel Espinosa and Maria Manuela Portela

Atmosphere 2025, 16(5), 514; https://doi.org/10.3390/atmos16050514 - 28 Apr 2025

Viewed by 41

Abstract

This study examines exceptional maximum temperature (T_max) events in mainland Portugal (1980–2024) using ERA5-Land reanalysis data at 1012 locations. To assess changes in the occurrence and temperature excess of exceptional events across two 22-year subperiods (or phases), percentile-based thresholds were adopted. [...] Read more.

This study examines exceptional maximum temperature (T_max) events in mainland Portugal (1980–2024) using ERA5-Land reanalysis data at 1012 locations. To assess changes in the occurrence and temperature excess of exceptional events across two 22-year subperiods (or phases), percentile-based thresholds were adopted. An inventive severity heatmap is used to illustrate exceptional T_max changes between the two phases, which constitutes an addition to climate change research. Locations are categorised in the heatmap according to whether they experienced (i) more occurrences and more temperature excess, (ii) more occurrences but less temperature excess, (iii) fewer occurrences but more temperature excess, or (iv) fewer occurrences and less temperature excess. From the historical (1980–2002) to the contemporary (2002–2024) phase, results indicate a significant increase in the severity of extreme heat, particularly in central and southern Portugal, with over 90% of locations exhibiting a rise in exceptional event occurrences. While the Student’s t-test indicated significant differences in both occurrence and temperature excess between the phases, Sen’s slope estimation showed steady upward trends. The results point to crucial regions in the interior and southern Portugal that have warmed the most, posing growing threats to agriculture, human health, and water resources. Although slight cooling trends were observed in a few northern and central coastal regions, the overall pattern highlights an increase in extreme heat. This research is particularly relevant given the recent changes in exceptional T_max identified in Portugal, aligning with broader climate change patterns and trends. Full article

(This article belongs to the Section Climatology)

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22 pages, 1509 KiB

Open AccessArticle

Geographically Aware Air Quality Prediction Through CNN-LSTM-KAN Hybrid Modeling with Climatic and Topographic Differentiation

by Yue Hu, Yitong Ding and Wenjing Jiang

Atmosphere 2025, 16(5), 513; https://doi.org/10.3390/atmos16050513 - 28 Apr 2025

Viewed by 50

Abstract

Air pollution poses a pressing global challenge, particularly in rapidly industrializing nations like China where deteriorating air quality critically endangers public health and sustainable development. To address the heterogeneous patterns of air pollution across diverse geographical and climatic regions, this study proposes a [...] Read more.

Air pollution poses a pressing global challenge, particularly in rapidly industrializing nations like China where deteriorating air quality critically endangers public health and sustainable development. To address the heterogeneous patterns of air pollution across diverse geographical and climatic regions, this study proposes a novel CNN-LSTM-KAN hybrid deep learning framework for high-precision Air Quality Index (AQI) time-series prediction. Through systematic analysis of multi-city AQI datasets encompassing five representative Chinese metropolises—strategically selected to cover diverse climate zones (subtropical to temperate), geographical gradients (coastal to inland), and topographical variations (plains to mountains)—we established three principal methodological advancements. First, Shapiro–Wilk normality testing (p < 0.05) revealed non-Gaussian distribution characteristics in the observational data, providing statistical justification for implementing Gaussian filtering-based noise suppression. Second, our multi-regional validation framework extended beyond conventional single-city approaches, demonstrating model generalizability across distinct environmental contexts. Third, we innovatively integrated Kolmogorov–Arnold Networks (KANs) with attention mechanisms to replace traditional fully connected layers, achieving enhanced feature weighting capacity. Comparative experiments demonstrated superior performance with a 23.6–59.6% reduction in Root-Mean-Square Error (RMSE) relative to baseline LSTM models, along with consistent outperformance over CNN-LSTM hybrids. Cross-regional correlation analyses identified PM2.5/PM10 as dominant predictive factors. The developed model exhibited robust generalization capabilities across geographical divisions (R² = 0.92–0.99), establishing a reliable decision-support platform for regionally adaptive air quality early-warning systems. This methodological framework provides valuable insights for addressing spatial heterogeneity in environmental modeling applications. Full article

(This article belongs to the Section Air Quality)

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36 pages, 28002 KiB

Open AccessArticle

Assessing the PM_2.5–O₃ Correlation and Unraveling Their Drivers in Urban Environment: Insights from the Bohai Bay Region, China

by Yan Nie, Yongxin Yan, Yuanyuan Ji, Rui Gao, Yanqin Ren, Fang Bi, Fanyi Shang, Jidong Li, Wanghui Chu and Hong Li

Atmosphere 2025, 16(5), 512; https://doi.org/10.3390/atmos16050512 - 28 Apr 2025

Viewed by 37

Abstract

Understanding the correlation between PM_2.5 and O₃ is critical for complex air pollution control. This study comprehensively analyzed PM_2.5 and O₃ pollution characteristics, uncovered spatiotemporal variations in their correlation, and investigated the driving mechanisms of their association in Dongying, [...] Read more.

Understanding the correlation between PM_2.5 and O₃ is critical for complex air pollution control. This study comprehensively analyzed PM_2.5 and O₃ pollution characteristics, uncovered spatiotemporal variations in their correlation, and investigated the driving mechanisms of their association in Dongying, a typical petrochemical city in China’s Bohai Bay region. Results showed that PM_2.5–O₃ correlation in Dongying exhibited significant seasonal variations, spatial patterns, and concentration threshold effects from 2017 to 2023. PM_2.5 and O₃ showed strong positive correlations in summer, negative in winter, and weak positive in spring/autumn, with strongest links in western areas. The strongest positive PM_2.5–O₃ correlation occurred in summer when PM_2.5 ≤ 35 μg·m⁻³ and O₃ >160 μg·m⁻³, while the strongest negative correlation was exhibited in winter with PM_2.5 > 75 μg·m⁻³ and O₃ ≤ 100 μg·m⁻³. Meteorological conditions (T > 20 °C, RH < 30%, wind speed < 1.73 m/s, O_x > 125 μg·m⁻³) and non-sea-breeze periods enhanced the PM_2.5–O₃ positive correlation. During the four typical pollution episodes, the positive PM_2.5–O₃ correlation in summer was propelled by synchronous increases in O₃ and secondary components via shared precursors. In autumn, strong positivity resulted from secondary component–O₃ correlations (r > 0.7) and dominance of secondary formation in PM_2.5. In winter, the negative correlation stemmed from primary emissions inhibiting photochemistry. Random forest analysis showed that O_x, RH, and T drove positive PM_2.5–O₃ correlation via photochemistry in summer, whereas winter primary emissions and NO titration caused negative correlation. This study offers guidance for the collaborative PM_2.5 and O₃ control in the petrochemical cities of the Bay region. Full article

(This article belongs to the Section Air Quality)

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19 pages, 19467 KiB

Open AccessArticle

Extreme Precipitation and Low-Lying Urban Flooding in Bahía Blanca, Argentina

by Natalia Verónica Revollo, Verónica Gil and Flavio Tiago Couto

Atmosphere 2025, 16(5), 511; https://doi.org/10.3390/atmos16050511 - 28 Apr 2025

Viewed by 114

Abstract

On the morning of 7 March 2025, the Argentine district of Bahía Blanca experienced a severe flooding that led to at least 15 fatalities. This study presents the main aspects of the event based on different data sources that helped to explain the [...] Read more.

On the morning of 7 March 2025, the Argentine district of Bahía Blanca experienced a severe flooding that led to at least 15 fatalities. This study presents the main aspects of the event based on different data sources that helped to explain the exceptional precipitation of about 300 mm and rapid flooding. The results indicated that Bahía Blanca district presented flooded areas of approximately 33 km² (1.4% of the total area) on 10 March, most of them concentrated in the non-urbanized zones. However, a total of 18 km² (0.8% of the total area) was still identified on 11 March, with a greater impact on the low-lying urban areas of the Bahía Blanca, General Daniel Cerri, and Ingeniero White towns. The likelihood of severe weather development was confirmed from instability indices. The very high moisture content along a low-level convergence line, jointly with upper-level divergence, contributed to deep convective cloud development that affected Bahía Blanca for at least 6 h. Increasing knowledge of urban floods from different data sources can support weather forecasts to provide timely warnings, essential to mitigate the adverse impacts of these extreme weather events on low-lying urban areas. Full article

(This article belongs to the Section Meteorology)

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18 pages, 12576 KiB

Open AccessArticle

Global Methane Retrieval, Monitoring, and Quantification in Hotspot Regions Based on AHSI/ZY-1 Satellite

by Tong Lu, Zhengqiang Li, Cheng Fan, Zhuo He, Xinran Jiang, Ying Zhang, Yuanyuan Gao, Yundong Xuan and Gerrit de Leeuw

Atmosphere 2025, 16(5), 510; https://doi.org/10.3390/atmos16050510 - 28 Apr 2025

Viewed by 83

Abstract

Methane is the second largest greenhouse gas. The detection of methane super-emitters and the quantification of their emission rates are necessary for the implementation of methane emission reduction policies to mitigate global warming. High-spectral-resolution satellites such as Gaofen-5 (GF-5), EMIT, GHGSat, and MethaneSAT [...] Read more.

Methane is the second largest greenhouse gas. The detection of methane super-emitters and the quantification of their emission rates are necessary for the implementation of methane emission reduction policies to mitigate global warming. High-spectral-resolution satellites such as Gaofen-5 (GF-5), EMIT, GHGSat, and MethaneSAT have been successfully employed to detect and quantify methane point source leaks. In this study, a matched filter (MF) algorithm is improved using data from the EMIT instrument and applied to data from the Advanced Hyperspectral Imager (AHSI) onboard the Ziyuan-1 (ZY-1) satellite. Validation by comparison with EMIT′s L2 XCH₄ products shows the good performance of the improved MF algorithm, in spite of the lower spectral resolution of AHSI/ZY-1 in comparison with other point source imagers. The improved MF algorithm applied to AHSI/ZY-1 data was used to detect and quantify methane super-emitters in global methane hotspot regions. The results show that the improved MF algorithm effectively suppresses noise in retrieval results over both land and ocean surfaces, enhancing algorithm robustness. Sixteen methane plumes were detected in global hotspot regions, originating from coal mines, oil and gas fields, and landfills, with emission rates ranging from 0.57 to 78.85 t/h. The largest plume was located at an offshore oil and gas field in the Gulf of Mexico, with instantaneous emissions nearly equal to the combined total of the other 15 plumes. The findings demonstrate that AHSI, despite its lower spectral resolution, can detect sources with emission rates as small as 571 kg/h and achieve faster retrieval speeds, showing significant potential for global methane monitoring. Additionally, this study highlights the need to focus on methane emissions from marine sources, alongside terrestrial sources, to efficiently implement reduction strategies. Full article

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

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15 pages, 2402 KiB

Open AccessArticle

Optimizing Livestock By-Products Storage to Reduce Ammonia and Greenhouse Gas Emissions Using Biochar and Wood Vinegar

by Alessandra Lagomarsino, Edoardo Verga, Massimo Valagussa, Stefano Rispoli, Filippo Rocchi, Claudia Becagli and Alberto Tosca

Atmosphere 2025, 16(5), 509; https://doi.org/10.3390/atmos16050509 - 28 Apr 2025

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Abstract

The environmental impact of livestock by-products presents significant challenges, particularly in regions with intensive farming and high pollution levels, such as the Po Valley. This study evaluated the effectiveness of biochar and wood vinegar in reducing gaseous emissions during the laboratory-scale storage of [...] Read more.

The environmental impact of livestock by-products presents significant challenges, particularly in regions with intensive farming and high pollution levels, such as the Po Valley. This study evaluated the effectiveness of biochar and wood vinegar in reducing gaseous emissions during the laboratory-scale storage of livestock slurry, digestate, and liquid fractions. Various types and applications of biochar, both with and without wood vinegar, were tested across three independent incubation periods of varying durations. The results showed that ammonia (NH₃) emissions were lower from slurry compared to raw digestate and the liquid fraction, while methane (CH₄) emissions exhibited the opposite trend. Pyrolysis biochar effectively reduced NH₃ emissions by 47% on average when applied as a 5 cm surface layer. However, its effectiveness was inconsistent when mixed into the material or when produced via gasification. Biochar activated with wood vinegar significantly reduced NH₃ emissions from both slurry and digestate by 61%, but it also led to increased emissions of CH₄ and CO₂. Nitrous oxide (N₂O) emissions were detected only after at least one month of incubation and were higher when biochar was used as a cover alone or when activated with wood vinegar. Overall, applying biochar as a cover and activating it with wood vinegar proved effective in reducing NH₃ emissions during the storage of livestock by-products. However, the effectiveness varied significantly depending on the type of biochar and its method of application, particularly with respect to CH₄ emissions, highlighting the need for careful consideration when using wood vinegar-activated biochar. Full article

(This article belongs to the Section Air Quality)

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30 pages, 4100 KiB

Open AccessArticle

Carbon Neutrality and Resilient Districts, a Common Strategy in European Union Countries in 2050

by Modeste Kameni Nematchoua, Minoson Sendrahasina Rakotomalala and Sigrid Reiter

Atmosphere 2025, 16(5), 508; https://doi.org/10.3390/atmos16050508 - 28 Apr 2025

Viewed by 157

Abstract

Confronted with the climate emergency, reducing CO₂ emissions has become a priority for all nations of the world because the follow-up of humanity depends on it. Most European Union (EU) member states have pledged to cut their net greenhouse gas emissions by [...] Read more.

Confronted with the climate emergency, reducing CO₂ emissions has become a priority for all nations of the world because the follow-up of humanity depends on it. Most European Union (EU) member states have pledged to cut their net greenhouse gas emissions by at least 55% by 2030 and reach full carbon neutrality by 2050, using 1990 as the baseline year. Despite this common effort, there is still a lack of effective decision-making on carbon neutrality strategies applied throughout the life cycle of a building in all EU countries. A common strategy is proposed in this study to fill this gap in the literature. The building sector is a real lever for reducing the carbon footprint and saving energy. Currently, the methodology for achieving large-scale carbon neutrality is well established. However, there is only a limited number of experts worldwide who have mastered this technology, making it challenging to develop a standardized approach for all nations. The absence of extensive, regular, and consistent data on carbon emissions has considerably hindered the understanding of the root causes of climate change at both the building and neighborhood levels. Is it not it time to break this barrier? With this in mind, this study was carried out with the intention of proposing a common method to achieve carbon neutrality at the neighborhood scale in European Union countries. The most significant parameters having a direct impact on carbon emissions have facilitated the adaptation of the three types of neighborhood in the different capitals of the EU countries, in particular, local building materials, microclimate, the energy mix of each country, and the mode of daily transport. The life cycle assessment of the three districts was conducted using the Plaides LCAv6.25.3 tool in combination with Meteonorm software version 8.2.0, considering a 100-year lifespan for the buildings. In addition, the cost of the various environmental impacts is assessed based on the monetary indicators for European Committee for Standardization indicators method. The main results showed that the distribution of carbon dioxide is 73.3% higher in urban areas than in sustainable neighborhoods and 39.0% higher in urban districts than in rural districts. Nearly zero emissions in the next decade are again possible by applying the scenario involves global warming combined with the complete (100%) renovation of all buildings and the transition to 100% electric vehicles along with the use of solar panels. This strategy makes it possible to reduce between 90.1% and 99.9% of the emission rate in residential districts regarding EU countries. Full article

(This article belongs to the Section Climatology)

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17 pages, 2511 KiB

Open AccessArticle

Can GCMs Simulate ENSO Cycles, Amplitudes, and Its Teleconnection Patterns with Global Precipitation?

by Chongya Ma, Jiaqi Li, Yuanchun Zou, Jiping Liu and Guobin Fu

Atmosphere 2025, 16(5), 507; https://doi.org/10.3390/atmos16050507 - 27 Apr 2025

Viewed by 101

Abstract

The ability of a general circulation model (GCM) to capture the variability of El Niño–Southern Oscillation (ENSO) is not only a scientific issue of climate model performance, but also critical for climate change and variability impact studies. Here, we assess 48 CMIP5 GCMs [...] Read more.

The ability of a general circulation model (GCM) to capture the variability of El Niño–Southern Oscillation (ENSO) is not only a scientific issue of climate model performance, but also critical for climate change and variability impact studies. Here, we assess 48 CMIP5 GCMs for their skill in simulating ENSO interdecadal variability and its teleconnection with precipitation globally. The results show that (1) only 22 out of 48 GCMs display interdecadal variability that is similar to the observations; (2) the ensemble of the 48 GCMs captures the ENSO–precipitation teleconnection at the global scale; (3) no single GCM can capture the observed ENSO–precipitation teleconnection globally; and (4) a GCM that can realistically simulate ENSO variability does not necessarily capture the ENSO-precipitation teleconnection, and vice versa. The results could also be used by climate change impact studies to select suitable GCMs, especially for regions with a statistically significant teleconnection between ENSO and precipitation, as well as for the comparison of CMIP5 and CMIP6. Full article

(This article belongs to the Special Issue Novel Approaches to Predict Extreme Events in Atmospheric Flows: From Turbulence to Climate)

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13 pages, 1409 KiB

Open AccessArticle

Comparison of the Chemical Composition of the Middle Atmosphere During Energetic Particle Precipitation in January 2005 and 2012

by Grigoriy Doronin, Irina Mironova and Eugene Rozanov

Atmosphere 2025, 16(5), 506; https://doi.org/10.3390/atmos16050506 - 27 Apr 2025

Viewed by 90

Abstract

We compare enhancements of mesospheric volume mixing ratios of hydroperoxyl radical

{HO}_{2}

and nitric acid

{HNO}_{3}

, as well as ozone depletion in the Northern Hemisphere (NH) polar night regions during energetic particle precipitation (EPP) in January of 2005 and 2012. [...] Read more.

We compare enhancements of mesospheric volume mixing ratios of hydroperoxyl radical

{HO}_{2}

and nitric acid

{HNO}_{3}

, as well as ozone depletion in the Northern Hemisphere (NH) polar night regions during energetic particle precipitation (EPP) in January of 2005 and 2012. We utilize mesospheric observations of

{HO}_{2}

,

{HNO}_{3}

, and ozone from the Microwave Limb Sounder (MLS/Aura). During the second half of January 2005 and 2012, the GOES satellite identified strong solar proton events with virtually the same proton flux parameters. Geomagnetic disturbances in January of 2005 were stronger, with Dst decreasing up to 100 nT compared to January 2012 while the Dst drop did not exceed 70 nT. Comparison of observations made with the MLS/Aura shows the highest change of

{HO}_{2}

and

{HNO}_{3}

concentrations and also the deepest ozone destruction at the latitudinal range from

60^{\circ}

NH to

80^{\circ}

NH inside the north polar vortex right after the spike in energetic particle flux registered by GOES satellites. MLS/Aura observations show

{HNO}_{3}

maximum enhancements of about 1.90 ppb and 1.66 ppb around 0.5 hPa (about 55 km) in January 2005 and January 2012, respectively. The

{HO}_{x}

increases lead to short-term ozone destruction in the mesosphere, which is seen in MLS/Aura ozone data. The maximum

{HO}_{2}

enhancement is about 1.05 ppb and 1.62 ppb around 0.046 hPa (about 70 km) after the onset of EPP in the second half of January 2005 and January 2012, respectively. Ozone maximum depletion is observed around 0.02 hPa (about 75 km). Ozone recovery after EPP was much faster in January 2005 than in January 2012. Full article

(This article belongs to the Section Climatology)

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18 pages, 4932 KiB

Open AccessArticle

Exploration of the Reasons for the Decreases in O₃ Concentrations in Tai’an City Based on the Control of O₃ Precursor Emissions

by Yanfei Liu, Shaocai Yu, Qiao Shi, Zhe Song, Ningning Yao, Huan Xi, Lang Chen, Yanzhen Ge, Tongsuo Yang, Yan Wang, Jianmin Chen and Pengfei Li

Atmosphere 2025, 16(5), 505; https://doi.org/10.3390/atmos16050505 - 27 Apr 2025

Viewed by 93

Abstract

Due to the “One City, One Policy” for air pollution prevention and control measures, Tai’an City was the only city in Shandong Province with a year-on-year decrease in O₃ concentrations in 2022. In this study, the WRF-CMAQ model was used to simulate [...] Read more.

Due to the “One City, One Policy” for air pollution prevention and control measures, Tai’an City was the only city in Shandong Province with a year-on-year decrease in O₃ concentrations in 2022. In this study, the WRF-CMAQ model was used to simulate the O₃ concentrations in Tai’an and other inland cities in Shandong Province in September 2022, and the model evaluation method was applied to discover the differences in the O₃ concentrations between Tai’an and other cities. During the periods of high maximum daily 8 h average O₃ (MDA8 O₃), the model only overestimated the O₃ concentrations in Tai’an by 3.4% and underestimated those in other inland cities by −11.0% to −2.2%. Dozens of O₃ simulation scenarios were designed on the basis of the control of O₃ precursor emissions, and the results indicate that the O₃ precursor emissions in Tai’an were at a lower level. On this basis, the impacts of meteorological conditions and O₃ precursor emission changes on O₃ concentrations in Tai’an were quantified. Adverse meteorological conditions and changes in emissions from other inland cities led to a 49.5 µg/m³ increase in the mean MDA8 O₃ in Tai’an during the study period. However, the local emission reduction measures in Tai’an, to some extent, offset these adverse effects, reducing the mean MDA8 O₃ by 5.8 µg/m³. In summary, the Tai’an City might implement effective emission reduction measures during periods of high MDA8 O₃, thereby achieving a reduction in overall O₃ concentrations. This effort secured its leading position in Shandong Province’s O_3–8h-90per ranking in 2022. Full article

(This article belongs to the Section Air Quality)

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14 pages, 9672 KiB

Open AccessArticle

Temporal and Spatial Analysis of Pedestrian Count Data for Thermal Environmental Planning in Street Canyons

by Hideki Takebayashi and Taichi Hayakawa

Atmosphere 2025, 16(5), 504; https://doi.org/10.3390/atmos16050504 - 27 Apr 2025

Viewed by 110

Abstract

In this study, we analyzed the spatiotemporal characteristics of pedestrian behavior in street spaces using pedestrian count data—specifically, the number of pedestrians passing in front of infrared sensors installed throughout the downtown area. The analysis focused on three main questions: (1) whether the [...] Read more.

In this study, we analyzed the spatiotemporal characteristics of pedestrian behavior in street spaces using pedestrian count data—specifically, the number of pedestrians passing in front of infrared sensors installed throughout the downtown area. The analysis focused on three main questions: (1) whether the thermal environment affects pedestrian behavior, (2) how to characterize the spatiotemporal patterns of pedestrian activity, and (3) how to effectively present the results to urban planners and designers. A temporal and spatial analysis method was examined using hourly pedestrian count data over one year at more than 100 locations in the street canyon. The temporal characteristics of the pedestrian count data were classified into weekday and weekend clusters according to the peak hours within a day. The spatial characteristics of the pedestrian count data were clearly defined by distance from the station, office district, and commercial district, according to peak commuting, shopping, etc. Results from principal component analysis and cluster analysis did not reveal a significant influence of the thermal environment on the temporal variation in pedestrian counts. Instead, the data suggested that weekday versus weekend distinctions were the primary determinants of daily and annual patterns, while seasonal and weather-related factors had relatively minor effects. The analytical approach developed in this study represents a valuable and practical contribution that may be applicable to other urban contexts as well. Full article

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

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20 pages, 4553 KiB

Open AccessArticle

A Short-Term Prediction Method for Tropospheric Delay Products in PPP-RTK Based on Multi-Scale Sliding Window LSTM

by Linyu He, Xingyu Zhou, Hua Chen, Jie He, Runhua Chen and Jie Ding

Atmosphere 2025, 16(5), 503; https://doi.org/10.3390/atmos16050503 - 26 Apr 2025

Viewed by 88

Abstract

Tropospheric delay products play a critical role in achieving high-precision positioning in Precise Point Positioning Real-Time Kinematic (PPP-RTK) applications. The short-term prediction of these products remains a significant challenge that warrants further exploration. This study proposes a novel short-term prediction method for tropospheric [...] Read more.

Tropospheric delay products play a critical role in achieving high-precision positioning in Precise Point Positioning Real-Time Kinematic (PPP-RTK) applications. The short-term prediction of these products remains a significant challenge that warrants further exploration. This study proposes a novel short-term prediction method for tropospheric delay products in PPP-RTK applications, leveraging a multi-scale sliding window and Long Short-Term Memory (LSTM) network. The multi-scale sliding window approach effectively captures data features across different temporal scales, while LSTM, a well-established and robust time series forecasting technique, ensures the accurate modeling of temporal dependencies. The integration of these two methods significantly enhances the precision of short-term tropospheric delay predictions. Experimental analysis utilizing one week of data from the Hong Kong Continuously Operating Reference Stations (CORS) network demonstrates that the proposed method achieves a maximum prediction error of less than 1.5 cm. Furthermore, compared to the standard LSTM approach, the Root Mean Square Error (RMSE) values are improved by 18.9% and 36.6% for different reference values, respectively. PPP-RTK positioning experiments reveal that the predicted products generated by this method exhibit notable improvements in Root Mean Square (RMS) values for the east, north, and up directions, with enhancements of 10.7%, 19.1%, and 4.1%, respectively, over those obtained using the conventional LSTM method. These results comprehensively validate the effectiveness and superiority of the proposed approach. Full article

(This article belongs to the Special Issue GNSS Remote Sensing in Atmosphere and Environment (2nd Edition))

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16 pages, 5654 KiB

Open AccessArticle

Sizing Accuracy of Low-Cost Optical Particle Sensors Under Controlled Laboratory Conditions

by Prakash Gautam, Andrew Ramirez, Salix Bair, William Patrick Arnott, Judith C. Chow, John G. Watson, Hans Moosmüller and Xiaoliang Wang

Atmosphere 2025, 16(5), 502; https://doi.org/10.3390/atmos16050502 - 26 Apr 2025

Viewed by 161

Abstract

Low-cost particulate matter sensors have seen increased use for monitoring at personal and local levels due to their affordability, ease of operation, and high time resolution. However, the quality of data reported by these sensors can be questionable, and a thorough evaluation of [...] Read more.

Low-cost particulate matter sensors have seen increased use for monitoring at personal and local levels due to their affordability, ease of operation, and high time resolution. However, the quality of data reported by these sensors can be questionable, and a thorough evaluation of their performance is necessary. This study evaluated the particle sizing accuracy of several commonly used optical sensors, including the Alphasense optical particle counter (OPC), TSI DustTrak DRX aerosol monitor, Plantower PMS5003 sensor, and Sensirion SPS30 sensor, using laboratory-generated monodisperse particles. The OPC and DRX agreed partially with reference instruments and showed promise in detecting coarse-size particles. However, the PMS5003 and SPS30 did not correctly size fine and coarse particles. Furthermore, their reported mass distributions do not directly correspond to their number distribution. Despite these limitations, field measurements involving a dust storm period showed that the SPS30 correlated reasonably well with reference instruments for both PM_2.5 and PM₁₀, though the regression slopes differed significantly. These findings underscore the need for caution when interpreting data from low-cost optical sensors, particularly for coarse particles. Recommendations for improving the performance of these sensors are also provided. Full article

(This article belongs to the Section Aerosols)

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25 pages, 32208 KiB

Open AccessArticle

Spatio-Temporal Heterogeneity of Vegetation Coverage and Its Driving Mechanisms in the Agro-Pastoral Ecotone of Gansu Province: Insights from Multi-Source Remote Sensing and Geodetector

by Macao Zhuo, Jianyu Yuan, Jie Li, Guang Li and Lijuan Yan

Atmosphere 2025, 16(5), 501; https://doi.org/10.3390/atmos16050501 - 26 Apr 2025

Viewed by 90

Abstract

The agro-pastoral ecotone of Gansu Province, a critical component of the ecological security barrier in northern China, is characterized by pronounced ecological fragility and climatic sensitivity. Investigating vegetation dynamics in this region is essential for balancing ecological conservation and sustainable development. This study [...] Read more.

The agro-pastoral ecotone of Gansu Province, a critical component of the ecological security barrier in northern China, is characterized by pronounced ecological fragility and climatic sensitivity. Investigating vegetation dynamics in this region is essential for balancing ecological conservation and sustainable development. This study integrated MODIS/NDVI remote sensing data (2000–2020), climate, land, and anthropogenic factors, employing Sen’s slope analysis, coefficient of variation (Cv), Hurst index, geodetector modeling, and partial correlation analysis to systematically unravel the spatio-temporal evolution and driving mechanisms of vegetation coverage. Key findings revealed the following: (1) Vegetation coverage exhibited a significant increasing trend (0.05 decade⁻¹), peaking in 2018 (NDVI = 0.71), with a distinct north–south spatial gradient (lower values in northern areas vs. higher values in southern regions). Statistically significant greening trends (p < 0.05) were observed in 55.42% of the study area. (2) Interannual vegetation fluctuations were generally mild (Cv = 0.15), yet central regions showed 2–3 times higher variability than southern/northwestern areas. Future projections (H = 0.62) indicated sustained NDVI growth. (3) Climatic factors dominated vegetation dynamics, with sunshine hours and precipitation exhibiting the strongest explanatory power (q = 0.727 and 0.697, respectively), while the elevation–precipitation interaction achieved peak explanatory capacity (q = 0.845). (4) NDVI correlated positively with precipitation in 43.62% of the region (r_mean = 0.47), whereas average temperature, maximum temperature, ≥10 °C accumulated temperature, and sunshine hours suppressed vegetation growth (r_mean = −0.06 to −0.42), confirming precipitation as the primary driver of regional vegetation recovery. The multi-scale analytical framework developed here provides methodological and empirical support for precision ecological governance in climate-sensitive transitional zones, particularly for optimizing ecological barrier functions in arid and semi-arid regions. Full article

(This article belongs to the Topic Climate Change and Environmental Sustainability, 4th Edition)

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25 pages, 21137 KiB

Open AccessArticle

Enhancing Maritime Navigation: A Global Navigation Satellite System (GNSS) Signal Quality Monitoring System for the North-Western Black Sea

by Petrica Popov, Maria Emanuela Mihailov, Lucian Dutu and Dumitru Andrescu

Atmosphere 2025, 16(5), 500; https://doi.org/10.3390/atmos16050500 (registering DOI) - 26 Apr 2025

Viewed by 147

Abstract

Global Navigation Satellite Systems (GNSSs) are the primary source of information for Positioning, Navigation, and Timing (PNT) in the maritime sector; however, they are vulnerable to unintentional or deliberate interference, such as jamming, spoofing, or meaconing. The continuous monitoring of GNSS signals is [...] Read more.

Global Navigation Satellite Systems (GNSSs) are the primary source of information for Positioning, Navigation, and Timing (PNT) in the maritime sector; however, they are vulnerable to unintentional or deliberate interference, such as jamming, spoofing, or meaconing. The continuous monitoring of GNSS signals is crucial for vessels and mobile maritime platforms to ensure the integrity, availability, and accuracy of positioning and navigation services. This monitoring is essential for guaranteeing the safety and security of navigation and contributes to the accurate positioning of vessels and platforms involved in hydrographic and oceanographic research. This paper presents the implementation of a complex system for monitoring the quality of signals within the GNSS spectrum at the Maritime Hydrographic Directorate (MHD). The system provides real-time analysis of signal parameters from various GNSSs, enabling alerts in critical situations and generating statistics and reports. It comprises four permanent stations equipped with state-of-the-art GNSS receivers, which integrate a spectrum analyzer and store raw data for post-processing. The system also includes software for monitoring the GNSS spectrum, detecting interference events, and visualizing signal quality data. Implemented using a Docker-based platform to enable efficient management and distribution, the software architecture consists of a reverse proxy, message broker, front-end, authorization service, GNSS orchestrator, and GNSS monitoring module. This system enhances the quality of command, control, communications, and intelligence decisions for planning and execution. It has demonstrated a high success rate in detecting and localizing jamming and spoofing events, thereby improving maritime situational awareness and navigational safety. Future development could involve installing dedicated stations to locate interference sources. Full article

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

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28 pages, 3520 KiB

Open AccessArticle

CIR-Driven Geomagnetic Storm and High-Intensity Long-Duration Continuous AE Activity (HILDCAA) Event: Effects on Brazilian Equatorial and Low-Latitude Ionosphere—Observations and Modeling

by Samuel Abaidoo, Virginia Klausner, Claudia Maria Nicoli Candido, Valdir Gil Pillat, Stella Pires de Moraes Santos Ribeiro Godoy, Fabio Becker-Guedes, Josiely Aparecida do Espírito Santo Toledo and Laura Luiz Trigo

Atmosphere 2025, 16(5), 499; https://doi.org/10.3390/atmos16050499 - 26 Apr 2025

Viewed by 68

Abstract

This paper investigates the effects of a Corotating Interaction Region (CIR)/High-Speed Stream (HSS)-driven geomagnetic storm from 13 to 23 October 2003, preceding the well-known Halloween storm. This moderate storm exhibited a prolonged recovery phase and persistent activity due to a High-Intensity Long-Duration Continuous [...] Read more.

This paper investigates the effects of a Corotating Interaction Region (CIR)/High-Speed Stream (HSS)-driven geomagnetic storm from 13 to 23 October 2003, preceding the well-known Halloween storm. This moderate storm exhibited a prolonged recovery phase and persistent activity due to a High-Intensity Long-Duration Continuous AE Activity (HILDCAA) event. We focus on low-latitude ionospheric responses induced by Prompt Penetration Electric Fields (PPEFs) and Disturbance Dynamo Electric Fields (DDEFs). To assess these effects, we employed ground-based GNSS receivers, Digisonde data, and satellite observations from ACE, TIMED, and SOHO. An empirical model by Scherliess and Fejer (1999) was used to estimate equatorial plasma drifts and assess disturbed electric fields. Results show a ∼120 km uplift in hmF2 due to PPEF, expanding the Equatorial Ionization Anomaly (EIA) crest beyond 20° dip latitude. DDEF effects during HILDCAA induced sustained F-region oscillations (∼100 km). The storm also altered thermospheric composition, with [[O]/[N₂] enhancements coinciding with TEC increases. Plasma irregularities, inferred from the Rate of TEC Index (ROTI 0.5–1 TECU/min), extended from equatorial to South Atlantic Magnetic Anomaly (SAMA) latitudes. These results demonstrate prolonged ionospheric disturbances under CIR/HSS forcing and highlight the relevance of such events for understanding extended storm-time electrodynamics at low latitudes. Full article

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

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26 pages, 8794 KiB

Open AccessArticle

Cover Crop Effects on Greenhouse Gas Emissions and Global Warming Potential in Furrow-Irrigated Corn in the Lower Mississippi River Valley

by Diego Della Lunga, Kristofor R. Brye, Michael J. Mulvaney, Mike Daniels, Tabata de Oliveira, Beth Baker, Timothy Bradford, Jr. and Chandler M. Arel

Atmosphere 2025, 16(5), 498; https://doi.org/10.3390/atmos16050498 - 25 Apr 2025

Viewed by 176

Abstract

Corn (Zea mays) production systems are described as high risk for emissions of greenhouse gases (GHG) due to large fertilizer inputs. Conservation practices, such as cover crop (CC), can limit the effects of agricultural activities on GHGs while increasing carbon and [...] Read more.

Corn (Zea mays) production systems are described as high risk for emissions of greenhouse gases (GHG) due to large fertilizer inputs. Conservation practices, such as cover crop (CC), can limit the effects of agricultural activities on GHGs while increasing carbon and nitrogen storage. The objective of the study was to assess the effects of cover crops, i.e., with CC and no-CC, on GHG (i.e., CO₂, CH₄, and N₂O) emissions and global warming potential (GWP) in furrow-irrigated corn in the Lower Mississippi River Valley. Gas sampling was conducted with an automated system that measured GHGs four times daily during the 2024 growing season in furrow-irrigated corn on a loam soil in Mississippi. Only CO₂ emissions differed (p < 0.05) by CC treatment, with soil respiration ~30% greater from CC than no-CC. Season-long emissions ranged from −0.22 to 0.30 kg CH₄ ha⁻¹ season⁻¹, 5.53 to 7.28 kg N₂O ha⁻¹ season⁻¹, with a GWP between 12,888 and 15,053 kg CO₂ eq. ha⁻¹ season⁻¹ from no-CC and CC, respectively. The role of CC as a conservation practice needs to be evaluated with soil and plant parameters. The beneficial effects of CC on soil physical and chemical properties likely outweigh a predictable increase in GHG emissions. Full article

(This article belongs to the Special Issue Gas Emissions from Soil)

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17 pages, 9827 KiB

Open AccessArticle

Construction of a NOx Emission Prediction Model for Hybrid Electric Buses Based on Two-Layer Stacking Ensemble Learning

by Jiangyan Qi, Xionghui Zou and Ren He

Atmosphere 2025, 16(5), 497; https://doi.org/10.3390/atmos16050497 - 25 Apr 2025

Viewed by 63

Abstract

To enhance the management of NOx emissions from hybrid electric buses, this paper develops an instantaneous NOx emission prediction model for hybrid electric buses based on a two-layer stacking ensemble learning method. Seventeen parameters, including operational characteristic parameters of hybrid electric buses, engine [...] Read more.

To enhance the management of NOx emissions from hybrid electric buses, this paper develops an instantaneous NOx emission prediction model for hybrid electric buses based on a two-layer stacking ensemble learning method. Seventeen parameters, including operational characteristic parameters of hybrid electric buses, engine operating parameters, and emission after-treatment device operating parameters are selected as input features for the model. The correlation analysis results indicate that the Pearson correlation coefficients of engine coolant temperature and selective catalytic reduction (SCR) after-treatment device temperature show a significant linear negative correlation with instantaneous NOx emission mass. The Mutual Information (MI) analysis reveals that engine intake air volume, SCR after-treatment device temperature and engine fuel consumption have strong nonlinear relationships with instantaneous NOx emission mass. The two-layer stacking ensemble learning model selects eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and an optimized BP neural network as base learners, with a linear regression model as the meta-learner, effectively predicting the instantaneous NOx emission mass of hybrid electric buses. The evaluation metrics of the proposed model—mean absolute error, root mean square error, and coefficient of determination—are 0.0068, 0.0283, and 0.9559, respectively, demonstrating a significant advantage compared to other benchmark models. Full article

(This article belongs to the Section Air Pollution Control)

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27 pages, 8230 KiB

Open AccessArticle

Development of High-Precision Local and Regional Ionospheric Models Based on Spherical Harmonic Expansion and Global Navigation Satellite System Data in Serbia

by Dušan Petković, Oleg Odalović, Aleksandra Nina, Miljana Todorović-Drakul, Aleksandra Kolarski, Sanja Grekulović and Stefan Krstić

Atmosphere 2025, 16(5), 496; https://doi.org/10.3390/atmos16050496 - 25 Apr 2025

Viewed by 189

Abstract

The relationship between ionospheric research and global navigation satellite systems (GNSS) can be analysed through two approaches. The direct approach utilises ionospheric models to mitigate its influence, while the indirect approach leverages GNSS data to model ionospheric parameters. This study presents an indirect [...] Read more.

The relationship between ionospheric research and global navigation satellite systems (GNSS) can be analysed through two approaches. The direct approach utilises ionospheric models to mitigate its influence, while the indirect approach leverages GNSS data to model ionospheric parameters. This study presents an indirect approach in which the total electron content (TEC), a fundamental parameter for ionospheric conditions, is modelled as a harmonic function using spherical harmonic (SH) expansion. Station-specific (local) and regional ionospheric models are developed by decomposing ionospheric influence into deterministic and stochastic components. GNSS data from seven evenly distributed stations in Serbia were used to estimate TEC coefficients. Local models were provided in the ION format as SH coefficients, allowing TEC determination at any epoch, while regional models had a

{0.5}^{\circ} \times {0.5}^{\circ}

spatial and 2 h temporal resolution. The TEC root mean square (RMS) values ranged from 0.2 to 0.5 TECU (total electron content unit), with a mean of 0.3 TECU. Validation against global ionospheric maps showed agreement within 5.0 TECU. The impact of the SH expansion degree and order on TEC values was also analysed. These results refine regional ionospheric modelling, improving GNSS positioning accuracy in Serbia and beyond. Full article

(This article belongs to the Special Issue GNSS Remote Sensing in Atmosphere and Environment (2nd Edition))

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24 pages, 26319 KiB

Open AccessArticle

Modeling PM_2.5 Levels Due to Combustion Activities and Fireworks in Quito (Ecuador) for Forecasting Using WRF-Chem

by Rene Parra

Atmosphere 2025, 16(5), 495; https://doi.org/10.3390/atmos16050495 - 25 Apr 2025

Viewed by 179

Abstract

PM_2.5 levels increase in cities during the first hours of the year due to combustion activities and the use of fireworks. In Quito (2800 masl), the capital of Ecuador, air quality records at the beginning of 2020 to 2025 (6 years) ranged [...] Read more.

PM_2.5 levels increase in cities during the first hours of the year due to combustion activities and the use of fireworks. In Quito (2800 masl), the capital of Ecuador, air quality records at the beginning of 2020 to 2025 (6 years) ranged between 13.4 and 217.8 µg m⁻³ (maximum mean levels for 24 h), most of them being higher than 15.0 µg m⁻³, the current recommended concentration by the World Health Organization (WHO), highlighting the need to decrease these emissions and promote actions to reduce the exposure to these extreme events. Air pollution forecasting as a preventive warning system could help achieve this objective. Therefore, the primary aim of this research was to analyze the variation in PM_2.5 levels in this city during the initial hours of the year to define, through numerical experiments, the spatiotemporal configuration of PM_2.5 emissions to reproduce the observed PM_2.5 levels and obtain insights to build an emission-based forecasting tool. For this purpose, we modeled atmospheric variables and the PM_2.5 levels using the Weather Research and Forecasting with Chemistry (WRF-Chem) model. Consistent with the behavior suggested by records of associated meteorological variables, the modeled planetary boundary layer height (PBLH) was generally lower in the city’s south compared with the center and the north. The records and modeled results indicated that in the south, the higher PM_2.5 levels were produced by higher emissions and lower values of the PBLH compared with the center and north, highlighting the importance of reducing the PM_2.5 emissions. The emission maps used for modeling the dispersion at the beginning of 2024 and 2025 are proposed as inputs for the future forecasting of the PM_2.5 levels at the start of the year, as preventive information for the public, to discourage, in advance, both combustion activities and the use of fireworks and to take action to avoid exposure. Full article

(This article belongs to the Special Issue Atmospheric Dispersion and Chemistry Models: Advances and Applications (2nd Edition))

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Atmosphere, Volume 16, Issue 5 (May 2025) – 39 articles

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