Atmosphere

19 pages, 1202 KB

Open AccessArticle

Comparison of the Applicability of Mainstream Objective Circulation Type Classification Methods in China

by Minjin Ma, Ran Chen and Xingyu Zhang

Atmosphere 2025, 16(11), 1231; https://doi.org/10.3390/atmos16111231 (registering DOI) - 24 Oct 2025

Circulation type classification (CTC) is an important method in atmospheric sciences, which reveals the relationship between atmospheric circulation and regional weather and climate. Accurate circulation classification helps to improve weather forecasting accuracy and supports climate change research. China has complex topography and significant [...] Read more.

Circulation type classification (CTC) is an important method in atmospheric sciences, which reveals the relationship between atmospheric circulation and regional weather and climate. Accurate circulation classification helps to improve weather forecasting accuracy and supports climate change research. China has complex topography and significant spatiotemporal variability in its circulation patterns, making the study of circulation type classification in this region highly significant. This study aims to evaluate the applicability of several mainstream objective CTC methods in the China region. We applied methods including T-mode principal component analysis (PCT), Ward linkage, K-means, and Self-Organizing Maps (SOM) to classify the sea-level pressure daily mean fields from 1993 to 2023 in the study area, and compared the classification results in terms of internal metrics, continuity, seasonal variation, separability of related meteorological variables (e.g., temperature, precipitation), and stability to spatiotemporal resolution. The results show that each method has its advantages in different contexts, with the K-means method showing the best overall performance. Additionally, an optimized approach combining PCT and K-means is proposed. Full article

(This article belongs to the Section Meteorology)

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26 pages, 982 KB

Open AccessArticle

Climate Change Through Urbanization: The Coupling Effects of Urbanization, Water Resources and Forests on Carbon Emissions

by Shengyuan Wang, Xiaolan Wu, Ying Liu and Rong Wang

Atmosphere 2025, 16(11), 1230; https://doi.org/10.3390/atmos16111230 (registering DOI) - 24 Oct 2025

Abstract

The purpose of this paper is to quantitatively study the impact mechanism of urbanization, water resources, and forestry system coupling on carbon emissions, and explore new ways to reduce carbon emissions, as complex relationships exist among urbanization, water resources, and forestry systems. Based [...] Read more.

The purpose of this paper is to quantitatively study the impact mechanism of urbanization, water resources, and forestry system coupling on carbon emissions, and explore new ways to reduce carbon emissions, as complex relationships exist among urbanization, water resources, and forestry systems. Based on the data of provincial regions in mainland China from 2015 to 2024, this paper analyzes the impact of urbanization, water resources, and forestry system coupling on carbon emissions by constructing the STIRPAT model. The findings reveal significant heterogeneity in the impact of the coupling degree among urbanization, water resources, and forestry systems on carbon emissions across Chinese provinces. Most regions exhibit insufficient carbon reduction effects. Enhancing the carbon mitigation effect through improving the coupling coordination of urbanization, water resources, and forestry systems presents a novel pathway toward achieving carbon neutrality during urbanization processes. Heterogeneity analysis further indicates that disparities in economic aggregate alter the mechanisms through which the STIRPAT model influences carbon emissions. The main contribution of this paper is to establish the evaluation index system of urbanization, water resources, and forestry development, analyze the mechanism of urbanization, water resources, and forestry coupling system affecting carbon emissions with the STIRPAT model, and explore new pathways for achieving carbon neutrality within urbanizing systems. Full article

(This article belongs to the Special Issue Urban Emissions and Climate Action: Strategies for a Low-Carbon Future)

29 pages, 1060 KB

Open AccessReview

Do Environmental Education Programs Reduce Pollution and Improve Air Quality? Impacts on Knowledge and Behavior Based on Evidence from a Mapping Review

by Rubia Truppel, Anderson D’Oliveira, Laura Canale, Luca Stabile, Giorgio Buonanno and Alexandro Andrade

Atmosphere 2025, 16(11), 1229; https://doi.org/10.3390/atmos16111229 - 23 Oct 2025

Abstract

This review investigates and analyzes the state of the art on scientific evidence related to educational interventions to improve air quality indoors and outdoors through a mapping review. The review followed proposed guidelines for mapping reviews in environmental sciences and the steps described [...] Read more.

This review investigates and analyzes the state of the art on scientific evidence related to educational interventions to improve air quality indoors and outdoors through a mapping review. The review followed proposed guidelines for mapping reviews in environmental sciences and the steps described in the Template for a Mapping Study Protocol. The search was conducted in PubMed, Web of Science, Embase, Cinahl, and Google Scholar with no language restrictions, and was completed in January 2025. Three filters were applied: search, selection with inclusion and exclusion criteria (PECOS strategy), and data extraction. Two independent reviewers assessed article eligibility, and disagreements were resolved by a third researcher. Twenty-four studies that met the eligibility criteria were included. Five research questions were answered. Studies published between 1977 and 2024 were included, totaling 7289 participants aged 12 to 85. The geographic distribution was concentrated in China (five studies) and the United States (four studies), followed by South Korea, India, Australia, and other countries, with fewer publications. The methodological predominance was experimental studies; observational studies were also analyzed, although less frequently. The period with the greatest increase in the number of publications was between 2020 and 2024. The educational methods most commonly used in the studies were lectures and the delivery of information leaflets. Particulate matter with diameters of 2.5 μm and 10 μm (PM_2.5 and PM₁₀) were the most widely investigated pollutants in the studies. From our analyses, it was observed that the educational interventions to improve air quality, adopted in the selected studies, resulted in the acquisition of knowledge about the environmental effects and the importance of individual actions. The changes in behavior included the adoption of more sustainable practices and an improvement in air quality in the environment, with a significant reduction in pollutant emissions. We conclude that interventions through environmental education demonstrate great potential to improve air quality. Based on the mapped evidence, governments and global policymakers can use this information to develop new strategies or improve existing ones to reduce air pollution in affected environments and regions. Full article

(This article belongs to the Section Air Quality)

15 pages, 1594 KB

Open AccessArticle

Improved Evaluation of Wind Turbine Lightning Exposure: Modeling Upward Leader Effects on Equivalent Collection Area

by Ning Yang, Ying Wen, Zheng Shi, Hongyu Zheng, Cuicui Ji and Maowen Liu

Atmosphere 2025, 16(11), 1228; https://doi.org/10.3390/atmos16111228 - 23 Oct 2025

Abstract

There has been a growing demand for clean energy in recent years, with the advancement of the carbon neutrality vision. Wind power has occupied a significant percentage of clean energy sources. Usually deployed in open fields, on mountaintops, and in offshore areas, wind [...] Read more.

There has been a growing demand for clean energy in recent years, with the advancement of the carbon neutrality vision. Wind power has occupied a significant percentage of clean energy sources. Usually deployed in open fields, on mountaintops, and in offshore areas, wind turbines are particularly vulnerable to lightning strikes due to their unique operational characteristics. Therefore, accurately evaluating the lightning strike risk of wind turbines is an important issue that should be addressed. Current IEC standards lack a physically grounded approach for calculating the equivalent collection area, leading to an overestimation of this value. This paper employs an upward leader initiation model to develop a novel calculation method for the equivalent collection area of wind turbines. By considering the impact of upward leader channel initiation and development, the model demonstrates accuracy through comparison with observational data (0.7761 strikes/year), showing only a −7.1% discrepancy. This study also examines the impact of various blade rotation angles, stepped leader speeds, and peak current of the return stroke on the equivalent collection area. Results indicate that the lightning strike distance specified in IEC standards underestimates the equivalent collection area due to neglecting the upward leader channel, resulting in significant differences compared to our approach, with a maximum deviation of up to 313.12%. Full article

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18 pages, 12394 KB

Open AccessArticle

Characteristics and Driving Factors of PM_2.5 Concentration Changes in Central China

by Yue Zhao, Ke Wang, Xiaoyong Liu, Qixiang Xu, Le Luo, Panpan Liu, Yanhua He, Yan Yu, Fangcheng Su and Ruiqin Zhang

Atmosphere 2025, 16(11), 1227; https://doi.org/10.3390/atmos16111227 - 23 Oct 2025

Abstract

Despite nationwide control efforts, central China experiences persistently high annual PM_2.5 concentrations (~50 μg/m³), which are particularly severe in January (exceeding 110 μg/m³). This study employs an integrated approach combining a Multiple Linear Regression (MLR) model derived from [...] Read more.

Despite nationwide control efforts, central China experiences persistently high annual PM_2.5 concentrations (~50 μg/m³), which are particularly severe in January (exceeding 110 μg/m³). This study employs an integrated approach combining a Multiple Linear Regression (MLR) model derived from random forest analysis with the WRF-CMAQ chemical transport modeling system to quantitatively disentangle the driving factors of PM_2.5 concentrations in central China. Key findings reveal significant spatiotemporal heterogeneity in anthropogenic contributions, evidenced by consistently higher north–south gradients in regression residuals (reflecting emission impacts), linked to spatially varying industrial and transportation influences. Critically, the reduction in anthropogenic impacts over six years was substantially smaller in winter (January: 27 to 23 μg/m³) compared to summer (15 to −18 μg/m³, July), highlighting the profound role of emissions in driving severe January pollution events. Furthermore, WRF-CMAQ simulations demonstrated that adverse meteorological conditions in January 2020 counteracted emission controls, causing a net increase in PM_2.5 of +13 μg/m³ relative to 2016, thereby offsetting ~68% of the reductions achieved through emission abatement (−19 μg/m³). Significant regional transport, especially affecting northern and central Henan, further weakened local control efficacy. These quantitative insights into the mechanisms of PM_2.5 pollution, particularly the counteracting effects of meteorology on emission reductions in critical winter periods, provide a vital scientific foundation for designing more effective and targeted air quality management strategies in central China. Full article

(This article belongs to the Special Issue Secondary Atmospheric Pollution Formations and Its Precursors)

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23 pages, 7295 KB

Open AccessArticle

An Artificial Intelligence-Driven Precipitation Downscaling Method Using Spatiotemporally Coupled Multi-Source Data

by Chao Li, Long Ma, Xing Huang, Chenyue Wang, Xinyuan Liu, Bolin Sun and Qiang Zhang

Atmosphere 2025, 16(11), 1226; https://doi.org/10.3390/atmos16111226 - 22 Oct 2025

Abstract

Addressing the challenges posed by sparse ground meteorological stations and the insufficient resolution and accuracy of reanalysis and satellite precipitation products, this study establishes a multi-source environmental feature system that precisely matches the target precipitation data resolution (1 km × 1 km). Based [...] Read more.

Addressing the challenges posed by sparse ground meteorological stations and the insufficient resolution and accuracy of reanalysis and satellite precipitation products, this study establishes a multi-source environmental feature system that precisely matches the target precipitation data resolution (1 km × 1 km). Based on this foundation, it innovatively proposes a Random Forest-based Dual-Spectrum Adaptive Threshold algorithm (RF-DSAT) for key factor screening and subsequently integrates Convolutional Neural Network (CNN) with Gated Recurrent Unit (GRU) to construct a Spatiotemporally Coupled Bias Correction Model for multi-source data (CGBCM). Furthermore, by integrating these technological components, it presents an Artificial Intelligence-driven Multi-source data Precipitation Downscaling method (AIMPD), capable of downscaling precipitation fields from 0.1° × 0.1° to high-precision 1 km × 1 km resolution. Taking the bend region of the Yellow River Basin in China as a case study, AIMPD demonstrates superior performance compared to bicubic interpolation, eXtreme Gradient Boosting (XGBoost), CNN, and Long Short-Term Memory (LSTM) networks, achieving improvements of approximately 1.73% to 40% in Nash-Sutcliffe Efficiency (NSE). It exhibits exceptional accuracy, particularly in extreme precipitation downscaling, while significantly enhancing computational efficiency, thereby offering novel insights for global precipitation downscaling research. Full article

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

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29 pages, 1817 KB

Open AccessArticle

Examination of Long-Term Temperature Change in Türkiye: Comparative Evaluation of an Advanced Quartile-Based Approach and Traditional Trend Detection Methods

by Omer Levend Asikoglu, Harun Alp, Ibrahim Temel and Pegah Kamali

Atmosphere 2025, 16(11), 1225; https://doi.org/10.3390/atmos16111225 - 22 Oct 2025

Abstract

The fact that 2023 and subsequently 2024 were the hottest years in history makes it even more important to monitor temperature changes over time. In this study, trends in the mean, maximum, and minimum temperature data of 81 provinces in Türkiye were examined [...] Read more.

The fact that 2023 and subsequently 2024 were the hottest years in history makes it even more important to monitor temperature changes over time. In this study, trends in the mean, maximum, and minimum temperature data of 81 provinces in Türkiye were examined using three traditional methods (Mann–Kendall, Linear Regression Analysis and Sen’s slope), one innovative method (ITA), and the QuarTrend (QT) method proposed in this study, which uses quartiles of the data series. The objectives of this research are (1) to determine and evaluate the long-term temperature trends in Türkiye (1960–2022) and (2) to comparatively evaluate the trend results of the proposed QT method, traditional trend detection methods, and ITA. In the study, a statistically significant (p < 0.05) increasing trend was found in the mean (0.027 °C/year), maximum (0.031 °C/year), and minimum (0.038 °C/year) annual temperatures of Türkiye. While traditional trend tests detected similar trends with ITA and QT for mean temperatures; ITA and QT detected more trends than traditional methods for maximum and minimum temperatures. The results have direct implications for the impacts of climate change in the study region. The results have the potential to support the development of climate-resilient and adaptive policies for effective water resource planning and management to sustain the environment and agricultural productivity in Türkiye. Full article

(This article belongs to the Section Meteorology)

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25 pages, 7312 KB

Open AccessArticle

Sensitivity of Airborne Methane Retrieval Algorithms (MF, ACRWL1MF, and DOAS) to Surface Albedo and Types: Hyperspectral Simulation Assessment

by Jidai Chen, Ding Wang, Lizhou Huang and Jiasong Shi

Atmosphere 2025, 16(11), 1224; https://doi.org/10.3390/atmos16111224 - 22 Oct 2025

Abstract

Methane (CH₄) emissions are a major contributor to greenhouse gases and pose significant challenges to global climate mitigation efforts. The accurate determination of CH₄ concentrations via remote sensing is crucial for emission monitoring but remains impeded by surface spectral heterogeneity—notably [...] Read more.

Methane (CH₄) emissions are a major contributor to greenhouse gases and pose significant challenges to global climate mitigation efforts. The accurate determination of CH₄ concentrations via remote sensing is crucial for emission monitoring but remains impeded by surface spectral heterogeneity—notably albedo variations and land cover diversity. This study systematically assessed the sensitivity of three mainstream algorithms, namely, matched filter (MF), albedo-corrected reweighted-L1-matched filter (ACRWL1MF), and differential optical absorption spectroscopy (DOAS), to surface type, albedo, and emission rate through high-fidelity simulation experiments, and proposed a dynamic regularized adaptive matched filter (DRAMF) algorithm. The experiments simulated airborne hyperspectral imagery from the Airborne Visible/InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) with known CH₄ concentrations over diverse surfaces (including vegetation, soil, and water) and controlled variations in albedo through the large-eddy simulation (LES) mode of the Weather Research and Forecasting (WRF) model and the MODTRAN radiative transfer model. The results show the following: (1) MF and DOAS have higher true positive rates (TP > 90%) in high-reflectivity scenarios, but the problem of false positives is prominent (TN < 52%); ACRWL1MF significantly improves the true negative rate (TN = 95.9%) through albedo correction but lacks the ability to detect low concentrations of CH₄ (TP = 63.8%). (2) All algorithms perform better at high emission rates (1000 kg/h) than at low emission rates (500 kg/h), but ACRWL1MF performs more robustly in low-albedo scenarios. (3) The proposed DRAMF algorithm improves the F1 score (0.129) by about 180% compared to the MF and DOAS algorithms and improves TP value (81.4%) by about 128% compared to the ACRWL1MF algorithm through dynamic background updates and an iterative reweighting mechanism. In practical applications, the DRAMF algorithm can also effectively monitor plumes. This research indicates that algorithms should be selected considering the specific application scenario and provides a direction for technical improvements (e.g., deep learning model) for monitoring gas emission. Full article

(This article belongs to the Special Issue Satellite Remote Sensing Applied in Atmosphere (3rd Edition))

23 pages, 11555 KB

Open AccessArticle

Precipitation Variation Characteristics in Gannan Prefecture, China: Application of the Innovative Trend Analysis and the BEAST (Bayesian Estimator of Abrupt Change, Seasonality, and Trend) Ensemble Algorithm

by Hui Zhou, Linjing Wei and Yanqiang Cui

Atmosphere 2025, 16(11), 1223; https://doi.org/10.3390/atmos16111223 - 22 Oct 2025

Abstract

This study examined the trend changes as well as the spatial distribution of average precipitation and the abrupt change characteristics of precipitation in Gannan Prefecture, China, using daily precipitation monitoring data from 1980 to 2021 at eight meteorological stations. Analytical methods employed included [...] Read more.

This study examined the trend changes as well as the spatial distribution of average precipitation and the abrupt change characteristics of precipitation in Gannan Prefecture, China, using daily precipitation monitoring data from 1980 to 2021 at eight meteorological stations. Analytical methods employed included the climate change trend rate, anomaly analysis, Innovative Trend Analysis (ITA), ITA-change boxes (ITA-CB), ArcGIS technology, and BEAST Ensemble Algorithm. Long-term average precipitation variability was comprehensively analyzed across multiple temporal scales. Results indicated that over the 42 years, interannual precipitation exhibited a significant increasing trend, with an annual rate of 14.363 mm/decade, and abrupt changes were detected in 1984, 2003, and 2018. The distribution of average precipitation varied substantially from year to year. July was the month with the highest average monthly precipitation, and December was the month with the lowest. Summer precipitation contributed the most to annual totals (51.33%), whereas winter precipitation contributed the least (2.01%). Interdecadal precipitation exhibited a pattern of an initial decrease followed by an increase over the study period. Based on the mean and standard deviation of the series’ first half, which was divided by the ITA method, we established a three-category classification for mean precipitation (low, medium, and high). Annual average and seasonal average precipitation showed non-monotonic variations. While the overall trend of annual average precipitation showed a modest augmentation, the increasing tendencies in the middle-value and high-value categories slowed. In spring, the decreasing trend in high-value categories slowed. In summer, decreasing trends in middle-value categories and overall zones slowed, with an enhanced increasing trend observed in autumn and winter overall. At the spatial scale, the average precipitation across Gannan Prefecture exhibited a decreasing trend from south to north. Higher precipitation was recorded at meteorological stations in the southwest (Maqu), west (Luqu), and south (Diebu), primarily influenced by the interaction between the Qinghai–Tibetan Plateau monsoon and westerly circulation, as well as regional topographic effects. The research findings have significant implications for agricultural and pastoral production planning and sustainable economic development in Gannan Prefecture, China. Full article

(This article belongs to the Section Climatology)

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28 pages, 5659 KB

Open AccessArticle

Airborne Microplastics: Source Implications from Particulate Matter Composition

by Hiroyuki Sasaki, Tsukasa Takahashi, Mari Futami, Tomomi Endo, Mizuho Hirano, Yuka Kotake and Kim-Oanh Pham

Atmosphere 2025, 16(11), 1222; https://doi.org/10.3390/atmos16111222 - 22 Oct 2025

Abstract

Microplastics (MPs) are emerging pollutants detected in diverse environments and human tissues. Among them, airborne MPs (AMPs) remain poorly characterized due to limited data and methodological inconsistencies. Although regarded as analogous to particulate matter (PM), detailed comparisons with its components are scarce. To [...] Read more.

Microplastics (MPs) are emerging pollutants detected in diverse environments and human tissues. Among them, airborne MPs (AMPs) remain poorly characterized due to limited data and methodological inconsistencies. Although regarded as analogous to particulate matter (PM), detailed comparisons with its components are scarce. To address this gap, this study implemented a unified and seasonal protocol for simultaneous measurement of AMPs and PM across three sites in Japan. AMPs were identified using micro-Raman spectroscopy, enabling polymer- and morphology-resolved analysis. A total of 106 AMPs were identified across all sites and seasons. Polyethylene (PE) was consistently dominant, followed by polyethylene terephthalate (PET) and polyamide (PA). Site-specific variation was evident, with certain polymers being relatively more abundant depending on the local environment. Feret diameter analysis showed a modal range of 4–6 μm, with fragments predominating over granular and fibrous particles. Significant correlations between AMP concentrations and PM components were determined, including syringaldehyde (SYAL), tungsten (W), cobalt (Co), and chromium (Cr), suggesting links to local sources, while indicating that AMP dynamics are not always aligned with PM behavior. This study provides one of the first integrated datasets of AMPs and PM components, offering insights into their occurrence, sources, and atmospheric relevance. Full article

(This article belongs to the Special Issue Micro- and Nanoplastics in the Atmosphere)

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38 pages, 4558 KB

Open AccessReview

Recent Advances in Wildland Fire Smoke Dynamics Research in the United States

by Yongqiang Liu, Warren E. Heilman, Brian E. Potter, Craig B. Clements, William A. Jackson, Nancy H. F. French, Scott L. Goodrick, Adam K. Kochanski, Narasimhan K. Larkin, Pete W. Lahm, Timothy J. Brown, Joshua P. Schwarz, Sara M. Strachan and Fengjun Zhao

Atmosphere 2025, 16(11), 1221; https://doi.org/10.3390/atmos16111221 - 22 Oct 2025

Abstract

Smoke plume dynamics involve various smoke processes and mechanics in the atmosphere and provide the scientific foundation for the development of tools to simulate and predict smoke and its environmental and human impacts. The increasing occurrence of wildfires and the demands for more [...] Read more.

Smoke plume dynamics involve various smoke processes and mechanics in the atmosphere and provide the scientific foundation for the development of tools to simulate and predict smoke and its environmental and human impacts. The increasing occurrence of wildfires and the demands for more extensive application of prescribed fires in the U.S. have posed great challenges and immediate actions for advancing smoke plume dynamics and improving smoke predictions and impact assessments to mitigate smoke impacts. Numerous efforts have been made recently to address these needs and challenges. This paper synthesizes advances in smoke plume dynamics research mainly conducted in the U.S. in the recent decade, identifies gaps, and suggests future research needs. The main advances include smoke data collections from comprehensive field campaigns, new satellite products, improved understanding of smoke plume properties and chemistry, structure and evolution, evaluation and improvement of smoke modeling and prediction systems, the development of coupled smoke models, and applications of machine-learning techniques. The major remaining gaps are the lack of comprehensive simultaneous measurements of smoke, fuels, fire, and atmospheric interactions during wildfires, high-resolution coupled modeling systems of these components, and real-time smoke prediction capacity. The findings from this synthesis study are expected to support smoke research and management to meet various challenges under increasing wildland fires and impacts. Full article

(This article belongs to the Section Air Quality)

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Atmosphere, Volume 16, Issue 11 (November 2025) – 11 articles

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