Journal Description
Atmosphere
Atmosphere
is an international, peer-reviewed, open access journal of scientific studies related to the atmosphere published monthly online by MDPI. The Italian Aerosol Society (IAS) and Working Group of Air Quality in European Citizen Science Association (ECSA) are affiliated with Atmosphere and their members receive a discount on the article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), Ei Compendex, GEOBASE, GeoRef, Inspec, CAPlus / SciFinder, Astrophysics Data System, and other databases.
- Journal Rank: CiteScore - Q2 (Environmental Science (miscellaneous))
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 16.1 days after submission; acceptance to publication is undertaken in 2.8 days (median values for papers published in this journal in the second half of 2024).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Testimonials: See what our editors and authors say about the Atmosphere.
- Companion journals for Atmosphere include: Meteorology and Aerobiology.
Impact Factor:
2.5 (2023);
5-Year Impact Factor:
2.6 (2023)
Latest Articles
Performance Evaluation of PM2.5 Forecasting Using SARIMAX and LSTM in the Korean Peninsula
Atmosphere 2025, 16(5), 524; https://doi.org/10.3390/atmos16050524 (registering DOI) - 29 Apr 2025
Abstract
Air pollution, particularly fine particulate matter (PM2.5), 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 PM2.5
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Air pollution, particularly fine particulate matter (PM2.5), 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 PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 forecasting accuracy.
Full article
(This article belongs to the Special Issue Novel Insights into Air Pollution over East Asia (Second Edition))
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
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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)
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
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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))
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
PM2.5 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
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PM2.5 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 PM2.5 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 PM2.5 at the ports of Incheon and Busan was generally high in nitrate composition; however, the ratio of sulfate was high on high PM2.5 concentration days. When implementing measures to reduce emissions related to ports and ships, forecasted PM2.5 and SO2 emissions showed substantial decreases in port areas as well as nearby land and sea areas. The associated decrease in the PM2.5 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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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
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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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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 NOX at multiple points
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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 NOX 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 NOX 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 NOX 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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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
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
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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 ( 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.
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(This article belongs to the Section Meteorology)
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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
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,
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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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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
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
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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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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
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
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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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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
Abstract
This study examines exceptional maximum temperature (Tmax) 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.
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This study examines exceptional maximum temperature (Tmax) 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 Tmax 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 Tmax identified in Portugal, aligning with broader climate change patterns and trends.
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(This article belongs to the Section Climatology)
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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
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
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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.
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(This article belongs to the Section Air Quality)
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Open AccessArticle
Assessing the PM2.5–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
Abstract
Understanding the correlation between PM2.5 and O3 is critical for complex air pollution control. This study comprehensively analyzed PM2.5 and O3 pollution characteristics, uncovered spatiotemporal variations in their correlation, and investigated the driving mechanisms of their association in Dongying,
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Understanding the correlation between PM2.5 and O3 is critical for complex air pollution control. This study comprehensively analyzed PM2.5 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 PM2.5–O3 correlation in Dongying exhibited significant seasonal variations, spatial patterns, and concentration threshold effects from 2017 to 2023. PM2.5 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 PM2.5–O3 correlation occurred in summer when PM2.5 ≤ 35 μg·m−3 and O3 >160 μg·m−3, while the strongest negative correlation was exhibited in winter with PM2.5 > 75 μg·m−3 and O3 ≤ 100 μg·m−3. Meteorological conditions (T > 20 °C, RH < 30%, wind speed < 1.73 m/s, Ox > 125 μg·m−3) and non-sea-breeze periods enhanced the PM2.5–O3 positive correlation. During the four typical pollution episodes, the positive PM2.5–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 PM2.5. In winter, the negative correlation stemmed from primary emissions inhibiting photochemistry. Random forest analysis showed that Ox, RH, and T drove positive PM2.5–O3 correlation via photochemistry in summer, whereas winter primary emissions and NO titration caused negative correlation. This study offers guidance for the collaborative PM2.5 and O3 control in the petrochemical cities of the Bay region.
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(This article belongs to the Section Air Quality)
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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
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
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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.
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(This article belongs to the Section Meteorology)
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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
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
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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.
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(This article belongs to the Special Issue Feature Papers in Atmospheric Techniques, Instruments, and Modeling)
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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
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
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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.
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(This article belongs to the Section Air Quality)
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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
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
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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.
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(This article belongs to the Section Climatology)
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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
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
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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.
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(This article belongs to the Special Issue Novel Approaches to Predict Extreme Events in Atmospheric Flows: From Turbulence to Climate)
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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
Abstract
We compare enhancements of mesospheric volume mixing ratios of hydroperoxyl radical and nitric acid , as well as ozone depletion in the Northern Hemisphere (NH) polar night regions during energetic particle precipitation (EPP) in January of 2005 and 2012.
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We compare enhancements of mesospheric volume mixing ratios of hydroperoxyl radical and nitric acid , 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 , , 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 and concentrations and also the deepest ozone destruction at the latitudinal range from NH to NH inside the north polar vortex right after the spike in energetic particle flux registered by GOES satellites. MLS/Aura observations show 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 increases lead to short-term ozone destruction in the mesosphere, which is seen in MLS/Aura ozone data. The maximum 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.
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(This article belongs to the Section Climatology)
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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
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
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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 O3–8h-90per ranking in 2022.
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(This article belongs to the Section Air Quality)
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