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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 19.7 days after submission; acceptance to publication is undertaken in 2.7 days (median values for papers published in this journal in the second half of 2025).
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 Atmosphere.
Companion journals for Atmosphere include: Meteorology and Aerobiology.

Impact Factor: 2.3 (2024); 5-Year Impact Factor: 2.5 (2024)

Imprint Information Journal Flyer Open Access ISSN: 2073-4433

Latest Articles

29 pages, 1962 KB

Open AccessArticle

Effects of Green Plants on the Indoor Environment: Real-Life Case Studies in Italian Schools and Office Spaces

by Simone Putzolu, Rita Baraldi, Luisa Neri, Alessandro Zaldei, Carolina Vagnoli, Beniamino Gioli, Adam Nawrocki and Cinzia De Benedictis

Atmosphere 2026, 17(6), 596; https://doi.org/10.3390/atmos17060596 (registering DOI) - 10 Jun 2026

Students and workers spend much of their day in school and office environments, where poor indoor air quality (IAQ) can negatively affect health and comfort. Indoor vegetation is increasingly proposed as a low-cost nature-based solution (NBS) to improve IAQ. This study evaluated the [...] Read more.

Students and workers spend much of their day in school and office environments, where poor indoor air quality (IAQ) can negatively affect health and comfort. Indoor vegetation is increasingly proposed as a low-cost nature-based solution (NBS) to improve IAQ. This study evaluated the effects of phytoremediation on IAQ and indoor microclimate in schools across different regions and educational levels, as well as in office environments, under real-world conditions. Several C₃ plants (e.g., Chamaedorea, Schefflera, Ficus, Epipremnum, Yucca, and Spathiphyllum) were used, with crassulacean acid metabolism (CAM) plants (Sansevieria) included in selected settings. Temperature, relative humidity, CO₂, PM_2.5, and PM₁₀ were continuously monitored using intercalibrated low-cost sensors in absence and presence of vegetation. A comparable plant configuration was implemented in offices to assess its effects on volatile organic compounds (VOC). Indoor greenery reduced particulate matter, especially PM10 (18–20%), and improved microclimatic conditions by lowering air temperature (1–2 °C) and increasing relative humidity (6–15%). However, CO₂ reductions were limited and context-dependent. In the tested office environments, plant introduction was associated with reduced total VOC concentrations (25–50%). Overall, our results further support that indoor vegetation constitutes a robust, cost-effective nature-based solution (NBS) capable of complementing conventional ventilation systems in both school and office environments. Full article

(This article belongs to the Special Issue Modelling of Indoor Air Quality and Thermal Comfort)

12 pages, 2941 KB

Open AccessArticle

Influence of North Atlantic Sea Surface Temperature Anomalies on Tibetan Plateau Vortex Frequency Variability

by Likang Xu, Panjie Qiao, Zaibo Zhao, Tingting Xue and Xu Li

Atmosphere 2026, 17(6), 595; https://doi.org/10.3390/atmos17060595 (registering DOI) - 10 Jun 2026

This study investigates the frequency of Tibetan Plateau vortices (TPVs) and their statistical relationship with global sea surface temperature (SST) anomalies. The results show that TPV frequency exhibits pronounced seasonal and interannual variability. Annual TPV frequency generally ranges from 50 to 70 events, [...] Read more.

This study investigates the frequency of Tibetan Plateau vortices (TPVs) and their statistical relationship with global sea surface temperature (SST) anomalies. The results show that TPV frequency exhibits pronounced seasonal and interannual variability. Annual TPV frequency generally ranges from 50 to 70 events, with short-lived TPVs, particularly those lasting two days, accounting for the majority of occurrences. TPV activity is most active during summer and relatively weak during autumn and winter. Lagged correlation analyses reveal that the North Atlantic exhibits the strongest statistical linkage with TPV frequency among all global ocean basins. After removing the linear trends, the maximum correlation occurs when North Atlantic SST anomalies lead TPV frequency anomalies by approximately two months, indicating a robust lagged relationship between the two variables. Further circulation analyses suggest that North Atlantic SST anomalies are closely associated with large-scale atmospheric circulation anomalies over the North Atlantic–Eurasian sector prior to TPV-active months. Anomalous geopotential height and wind fields at 500 hPa, together with upper-level wind anomalies at 200 hPa, indicate significant adjustments of the Eurasian midlatitude circulation and upper-level westerly jet associated with North Atlantic SST variability. During TPV-active months, enhanced upper-level divergence, strengthened upward motion, and intensified cyclonic anomalies emerge over the Tibetan Plateau, providing favorable dynamical conditions for TPV formation and development. Overall, the results reveal a statistically robust linkage between North Atlantic SST anomalies and TPV frequency variability and provide new insight into the associated large-scale circulation background over the Tibetan Plateau. Full article

(This article belongs to the Special Issue Simulation, Assessment, and Impacts of Extreme Hydroclimatic Events)

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33 pages, 9238 KB

Open AccessArticle

Atmospheric Ecological Index Prediction and Grade Zoning in the Qinling Mountains Based on Time-Series Models: A Case Study of Shangluo City

by Lei Wang, Jingyi Chen, Xiaogang Li, Hua Li, Shifa Zhao, Yaodong Guo and Xiaocun Zhang

Atmosphere 2026, 17(6), 594; https://doi.org/10.3390/atmos17060594 (registering DOI) - 9 Jun 2026

Mountain ecosystems are sensitive response units and critical ecological barriers to global climate change. Located in the mid-latitude climate transition zone, these ecosystems feature high ecological sensitivity and complex driving mechanisms, creating an urgent need to conduct long-sequence, high-precision dynamic assessments in order [...] Read more.

Mountain ecosystems are sensitive response units and critical ecological barriers to global climate change. Located in the mid-latitude climate transition zone, these ecosystems feature high ecological sensitivity and complex driving mechanisms, creating an urgent need to conduct long-sequence, high-precision dynamic assessments in order to support ecological conservation and climate adaptation decision-making. However, three key research gaps remain in the field: first, traditional assessments are dominated by static observation, lacking the capacity for long-sequence dynamic analysis and future projection; second, the coupled interaction mechanism among multiple ecological factors remains unclear, with insufficient quantitative and physical mechanism characterization; third, existing ecological zoning has not been validated for robustness, rendering it incapable of addressing climate disturbances and extreme scenarios. In order to study the regional atmospheric ecosystem, this study takes Shangluo in the eastern Qinling Mountains as the study area and constructs an integrated assessment framework integrating multi-dimensional diagnosis, simulation and projection, dynamic zoning and robustness validation based on long-sequence multi-factor data covering the years 1965–2024. The study aims to reveal the long-sequence evolution patterns and four-dimensional coupling mechanism of the Qinling Mountains atmospheric ecosystem, developing a reproducible and transferable dynamic assessment model. The results show that the study area exhibits the characteristic of elevation-dependent warming, and the correlation coefficients between elevation and air temperature, and between vegetation coverage and air quality reach −0.89 and −0.76, respectively.; ecological quality presents a spatial pattern of being high in the southwest and low in the northeast, with a coefficient of variation across the whole study area lower than 0.03. The results of 1000 Monte Carlo random disturbance validation runs show that even under intensified climate stress, the zoning pattern still maintains extremely strong disturbance resistance. This study reveals the steady-state multi-factor interaction mechanism in mountainous regions, addressing the defects of traditional static assessments that ignore ecosystem evolution and lag effects. The dynamic projection model constructed in this study can be transferred to similar mid-latitude mountainous regions worldwide, providing theoretical and technical support for regional ecological governance. Full article

(This article belongs to the Topic Big Data Analytics for Climate and Human Impacts on Terrestrial Ecosystems)

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18 pages, 6177 KB

Open AccessArticle

Impacts of Biomass Burning, Urbanization, and Regional Environmental Conditions on Air Quality in Medium-Sized Cities in Brazil

by Paula Florencio Ramires, Washington Luiz Félix Correia Filho, Rodrigo de Lima Brum and Flavio Manoel Rodrigues da Silva Júnior

Atmosphere 2026, 17(6), 593; https://doi.org/10.3390/atmos17060593 (registering DOI) - 9 Jun 2026

Introduction: International studies have demonstrated a positive impact on air quality associated with the presence of green areas in urban conglomerates. However, in Brazil, studies addressing the impacts of urban green areas on air quality are still incipient and are predominantly focused on [...] Read more.

Introduction: International studies have demonstrated a positive impact on air quality associated with the presence of green areas in urban conglomerates. However, in Brazil, studies addressing the impacts of urban green areas on air quality are still incipient and are predominantly focused on large urban centers. The objective of this study was to investigate the relationship between urban green areas, surface temperature (LST), and air quality across 15 medium-sized Brazilian cities. Methods: Concentrations of particulate matter fractions (PM₁, PM_2.5, and PM₁₀) were monitored from January 2023 to May 2024 using second data from low-cost sensors. The NDVI and both daytime and nighttime LST profiles were extracted via Google Earth Engine within a 1 km buffer zone surrounding each station via the Sentinel-2 and MODIS 11A1 satellite data, respectively. Spatial–temporal co-variation patterns were explored using principal component analysis (PCA). To model these dynamics while controlling for spatial dependencies, a multi-criteria framework compared linear models (simple linear regression (LM) and linear mixed (LMM)) and generalized models (generalized additive (GAM) and generalized additive mixed (GAMM)). Results: The results revealed a positive relationship between NDVI and PM_2.5 and PM₁₀ fractions in specific regions, while surface temperatures showed a direct association with finer particles (PM₁ and PM_2.5). The regression coefficient showed the significant association of PM2.5 with NDVI and nighttime LST (β = 1.330; IC 95%: [0.397; 2.270]; p = 0.005). The GAMM was the best-fitting model for all particle fractions, demonstrating that incorporating monitoring stations as random intercepts successfully controls for unmeasured local heterogeneity, while penalized splines accurately capture non-linear environmental factors. Conclusions: Although many studies have shown that green areas in temperate regions typically act as consistent sinks for particulate matter, our study revealed localized and seasonal responses in tropical urban landscapes. It should be noted that our study is conducted on a national scale and that the use of low-cost sensors and remote sensing does not allow us to distinguish between the localized microclimatic benefits of vegetation and the long-range transport of regional pollutants. Full article

(This article belongs to the Special Issue Air Quality and Its Impacts on Public Health)

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15 pages, 5786 KB

Open AccessArticle

Parallel Surface Renewal for Estimating Turbulent Fluxes in Vineyards and Almond Orchards

by Francesc Castellví, Juan M. Sánchez and Ramón López-Urrea

Atmosphere 2026, 17(6), 592; https://doi.org/10.3390/atmos17060592 (registering DOI) - 9 Jun 2026

The La Mancha region (a semi-arid area of southeast Spain) hosts the world’s highest concentration of vineyards and is also one of the regions with the largest areas devoted to almond tree cultivation. Viticulture and nut fruit trees (mainly almonds) are one of [...] Read more.

The La Mancha region (a semi-arid area of southeast Spain) hosts the world’s highest concentration of vineyards and is also one of the regions with the largest areas devoted to almond tree cultivation. Viticulture and nut fruit trees (mainly almonds) are one of the region’s principal sources of economic revenue. The Two-Source Energy Balance (TSEB) model can assist management of water resources. A simplified version of the TSEB approach (STSEB) was previously tested in a vineyard and almonds to estimate sensible heat (H) and latent heat (LE) fluxes using a parallel scheme method based on the Monin–Obukov similarity theory (MOST). This study introduces a method based on Surface Renewal (SR) theory to partition the sensible heat flux using low-frequency measurements as input. The latter was friendlier than the parallel MOST method under unstable conditions and than the series SR and MOST methods. The objective was to compare the MOST and SR models within a parallel scheme method. During the 2014 and 2015 growing season, measurements were collected in a 4 ha row crop drip-irrigated Tempranillo vineyard. Hourly sensible heat flux measured by an eddy covariance (EC) system and evapotranspiration (ET) registered by a 9 m² monolithic large weighting lysimeter were used as a reference. ET estimates were obtained as a residual of the energy balance equation (known as the residual method) using three methods for estimating sensible heat flux, H_SR, H_MOST and H_EC, yielding ET_SR-RE, ET_MOST-RE and ET_EC-RE, respectively. For sensible heat flux, the index of agreement (IA expressed in %) for 2014 and 2015 was 93% and 83%, respectively, using SR, and 84% and 78%, respectively, for MOST. This represents a 6–10% improvement using SR. For evapotranspiration, the ET_SR-RE and ET_MOST-RE IA showed similar performance in both years (around 88%), while ET_EC-RE yielded the best results (92% and 89% for 2014 and 2015, respectively). In addition, half-hourly EC fluxes, during the growing season of 2017, were used as a reference in an almond orchard. The SR sensible heat flux performed better (IA = 93%) than MOST (IA = 86%) in this case, whereas for the latent heat flux, the residual method performed the best, resulting in an IA of 81% for SR and of 78% for MOST. Overall, SR performed better than MOST, particularly under unstable conditions with wind speeds above 1 ms⁻¹. Full article

(This article belongs to the Section Biosphere/Hydrosphere/Land–Atmosphere Interactions)

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25 pages, 6771 KB

Open AccessArticle

Multi-Method Explainable AI Framework for Quantifying Traffic and Meteorological Contributions to Urban Air Pollution: A Case Study of Istanbul’s Bosphorus Bridge Corridor

by Enes Birinci, Hüseyin Özdemir and Ali Deniz

Atmosphere 2026, 17(6), 591; https://doi.org/10.3390/atmos17060591 (registering DOI) - 9 Jun 2026

Urban air pollution results from complex interactions between vehicle emissions, meteorological conditions, and atmospheric chemistry. While machine learning models achieve high accuracy in air quality prediction, their limited transparency hinders policy adoption. We present an integrated (M-ETAQI) framework combining multiple XAI techniques, temporal [...] Read more.

Urban air pollution results from complex interactions between vehicle emissions, meteorological conditions, and atmospheric chemistry. While machine learning models achieve high accuracy in air quality prediction, their limited transparency hinders policy adoption. We present an integrated (M-ETAQI) framework combining multiple XAI techniques, temporal decomposition, and causal inference to quantify traffic and meteorological contributions to PM₁₀, PM_2.5, NO_X, and NO₂ concentrations in the Istanbul FSM Bridge corridor (2022–2023 hourly data). Five machine learning models, including XGBoost, LightGBM, CatBoost, Random Forest, and CNN–LSTM–Attention, were trained with temporal cross-validation. SHAP, LIME, PDP, and ALE were applied for interpretability; STL decomposition isolated temporal components, and CCM tested causal links. Tree-based models achieved R² > 0.80 for all pollutants, with CatBoost reaching PM_2.5 R² = 0.876. SHAP confirmed Lag1 as the dominant feature. Wind speed had a significant negative effect on NO_X, while traffic contributed ~20% to NO_X, twice that of other pollutants. STL showed the trend component dominated total variance; NO₂ trend variance = 56.3%. CCM revealed wind speed as the strongest causal driver of NO_X (ρ = 0.37) and confirmed direct traffic–NO_X links. Knowledge distillation from CatBoost improved CNN–LSTM–Attention performance. The four XAI methods yielded consistent attributions, providing robust, cross-validated evidence for traffic management and air-quality policy. Full article

(This article belongs to the Section Air Quality)

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30 pages, 14210 KB

Open AccessArticle

Characterising Multivariate Air Pollution State Evolution in an Urban Atmosphere Using Deep-Learned Baseline Representations: London

by Arda Eraslan, David Topping, Dudley E. Shallcross, M. A. H. Khan and Aşan Bacak

Atmosphere 2026, 17(6), 589; https://doi.org/10.3390/atmos17060589 (registering DOI) - 8 Jun 2026

Urban air quality management has been playing a significant role due to its effects on public health and pollution characteristics of countries with constantly changing policies. Traditional approaches capture how much pollution is present but are unable to detect changes in the chemical [...] Read more.

Urban air quality management has been playing a significant role due to its effects on public health and pollution characteristics of countries with constantly changing policies. Traditional approaches capture how much pollution is present but are unable to detect changes in the chemical character of the atmosphere, the relationships between co-emitted species, the balance of photochemical processing, and the combustion fingerprint of emission sources. This study introduces a framework that identifies and diagnoses such evolutions within the pollutants of the atmosphere. A chemistry-aware Variational Autoencoder is trained on 19 multivariate pollution features (7 raw concentrations, 5 chemical ratios, 7 temporal gradients) at London Marylebone Road (urban roadside) and North Kensington (urban background) from 2015 to 2019, and tested on 2022–2025. A four-method ensemble framework (VAE reconstruction error, reconstruction probability, Isolation Forest, and statistical Z-score) requires ≥3 agreement to identify high-confidence departed pollution states. Per-feature decomposition of the reconstruction probability diagnoses the chemical character of each departure. At the roadside site, 14.5% of post-COVID hours fall within departed states, dominated by the CO/NO_x combustion ratio (513.2) and the photostationary state proxy (391.4), chemical relationships rather than individual concentrations. This indicates that at the point of emission, London’s fleet modernisation and Ultra Low Emission Zone (ULEZ) have changed the combustion fingerprint and photochemical equilibrium. The same structural indicators are carried over during the COVID-19 lockdown; however, O₃ rises 3.2× during the pandemic period, reflecting suppressed NO titration. Conversely, at the urban background site, where the departures are driven by concentrations and boundary-layer trapping (

r = - 0.659

), the combustion fingerprint of the atmosphere is invisible to detect (CO/NO_x

= - 45.0

). These findings indicate that London’s emission landscape has undergone fundamental transformations over the past decade, and the consequences of ULEZ and similar interventions or greater impacts of pandemic-related events are non-homogeneously distributed across the relevant region. Full article

(This article belongs to the Special Issue Advances in Air Pollution Data Analysis: From Classical Geostatistics to Big Data and Artificial Intelligence)

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Graphical abstract

21 pages, 2993 KB

Open AccessArticle

Global Insights into the Synergistic Characteristics of Methane and Nitrous Oxide Emissions from China’s Animal Husbandry and Their Policy Implications

by Lin Yang, Min Wang, Xiangzhao Feng and Ling Zhu

Atmosphere 2026, 17(6), 590; https://doi.org/10.3390/atmos17060590 - 7 Jun 2026

Livestock production is a major source of agricultural methane (CH₄) and nitrous oxide (N₂O), making the synergistic mitigation of these two gases essential for meeting climate targets. Based on the EDGAR emission database from 2000 to 2024, this study [...] Read more.

Livestock production is a major source of agricultural methane (CH₄) and nitrous oxide (N₂O), making the synergistic mitigation of these two gases essential for meeting climate targets. Based on the EDGAR emission database from 2000 to 2024, this study employs international comparisons, spatial analysis, and STIRPAT-based scenario projections to characterize emissions from China’s animal husbandry and explore pathways for synergistic mitigation. The results reveal that China’s livestock CH₄ emissions exhibited a trend of early-stage fluctuation followed by a late-stage rebound, while N₂O emissions fluctuated sharply. The two gases are strongly synergistic yet driven by distinct mechanisms. China accounts for the largest share of global emissions and exhibits a distinctive emission structure—with comparable contributions from enteric fermentation and rice paddies—setting it apart from both pasture-based and intensive developed countries. High-emission areas are becoming increasingly concentrated in northern production regions. Under the baseline scenario, CH₄ and N₂O emissions are projected to peak in 2032 and 2030, respectively; under an ultra-low-carbon scenario, both gases peak around 2029, at substantially lower levels. Achieving synergistic mitigation calls for a regionally differentiated framework that combines top-down governance with bottom-up participation from farmers, integrating enteric fermentation control with optimized manure management to support a low-carbon transition. Full article

(This article belongs to the Special Issue Solar Radiation, Aerosol, and Multiple Interactions Between Solar Radiation and Atmospheric Substances)

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31 pages, 3196 KB

Open AccessArticle

Event-Scale Directed Synchronization Networks of PM2.5–O₃ Compound Pollution in the Yangtze River Delta, China, 2015–2024: From Co-Occurrence to Coordinated Control

by Hanxing Zheng and Yiman Chen

Atmosphere 2026, 17(6), 588; https://doi.org/10.3390/atmos17060588 - 6 Jun 2026

PM2.5 and near-surface O₃ compound pollution is a major challenge for further air quality improvement in the Yangtze River Delta (YRD). Despite research on the chemical coupling mechanisms and concentration co-variation between PM2.5 and O₃, the directional linkages of compound [...] Read more.

PM2.5 and near-surface O₃ compound pollution is a major challenge for further air quality improvement in the Yangtze River Delta (YRD). Despite research on the chemical coupling mechanisms and concentration co-variation between PM2.5 and O₃, the directional linkages of compound pollution events among cities and the network mechanisms underlying their formation remain unclear. Here, we identified PM2.5–O₃ compound pollution events for 41 YRD cities from 2015 to 2024 using city-year-specific P80 dual-threshold criteria. We then constructed annual directed synchronization networks based on event-leading relationships and used temporal exponential random graph models to identify the formation mechanisms of significant leading ties. PM2.5–O₃ compound pollution events in the YRD generally decreased during 2015–2024, with characteristics shifting from high frequency, persistence, and strong intercity linkage in the early stage to lower frequency, weaker intensity, and continued episodic fluctuations. Directed event networks exhibited a clear stage-dependent evolution: network density, total edge weight, reciprocity, and local closure were relatively high during 2015–2018, networks became markedly sparse during 2020–2022, and a partial rebound occurred after 2023. Spatial backbone analysis indicated reorganization of the dominant linkage structure, shifting from the Shanghai–southern Jiangsu–northern Zhejiang coastal core toward the northern Jiangsu, Anhui, and interprovincial corridors. Key node analysis further revealed a clear functional differentiation among cities, with some cities acting as potential leading sources, some as receiving nodes, and several non-traditional core cities serving as cross-regional bridges. Significant leading ties were jointly shaped by reciprocity, local closures, temporal memory, economic development, industrial structure, and digital governance. Therefore, as well as a problem of co-occurrence, PM2.5–O₃ compound pollution in the YRD is a cross-city event-network process characterized by directionality, stage-dependent evolution, and differentiated urban roles. This study provides empirical evidence for dynamic joint prevention and control based on event linkages, urban roles, and cross-city coordination. Full article

(This article belongs to the Special Issue Coordinated Control of PM2.5 and O₃ and Its Impacts in China)

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23 pages, 4756 KB

Open AccessArticle

Long-Term Cross-Border PM2.5 Transport Coupling in Southeast Asia, 2003–2024

by Sornkitja Boonprong, Tunlawit Satapanajaru, Anak Khantachawana, Wangfei Zhang, Pariwate Varnakovida and Orrasa Rattana-amornpirom

Atmosphere 2026, 17(6), 587; https://doi.org/10.3390/atmos17060587 - 6 Jun 2026

Transboundary fine particulate matter (PM2.5) in Southeast Asia is commonly assessed using static source–receptor frameworks or descriptive associations that may not resolve how directional dependence changes through time under shifting meteorological conditions. This study examines regional PM2.5 as a time-varying, meteorology-adjusted directional coupling [...] Read more.

Transboundary fine particulate matter (PM2.5) in Southeast Asia is commonly assessed using static source–receptor frameworks or descriptive associations that may not resolve how directional dependence changes through time under shifting meteorological conditions. This study examines regional PM2.5 as a time-varying, meteorology-adjusted directional coupling system using monthly data for 2003–2024 from the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis, European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) meteorological covariates, climate controls, and administrative aggregation. Using a rolling-window directed network framework based on Peter and Clark Momentary Conditional Independence (PCMCI) causal discovery, we inferred lagged conditional-dependence networks from covariate-adjusted PM2.5 anomalies and summarized their structure at national and first-order administrative levels. The inferred network structure varies over time but retains measurable continuity across rolling windows. At the country level, cross-border links consistently account for a large share of the directed structure, indicating that PM2.5 variability within the study domain is strongly shaped by transboundary coupling rather than by country-contained dynamics alone. A recurrent backbone of country-level directional coupling corridors emerges, including persistent links among China, Indonesia, Myanmar, and Thailand. At the first administrative level, stable gateways and receptor basins become more evident, especially the bidirectional coupling corridor between Yunnan Province, China, and Shan State, Myanmar, which appears throughout the full window sequence. These results show that subnational structure can reveal transport-relevant coupling patterns that national summaries may conceal. The framework provides an interpretable basis for corridor-oriented monitoring and regime-aware early warning, while the inferred links should be interpreted as directional statistical dependence rather than direct emissions attribution or resolved physical transport pathways. Full article

(This article belongs to the Section Air Quality)

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21 pages, 2158 KB

Open AccessArticle

Hydrothermal Controls of Climate Extremes on Maize Yield Across Scales in Hilly Regions

by Yinxi Zhao, Yanzai Wang, Heng Wang and Yang Wang

Atmosphere 2026, 17(6), 586; https://doi.org/10.3390/atmos17060586 - 5 Jun 2026

This study examines the multi-scale relationships between extreme climate indices and maize yield from a hydrothermal perspective, across both temporal (long-term trends, interannual anomalies, and abrupt changes) and spatial (regional and grid) scales in the Chengdu–Chongqing region, using long-term meteorological (1985–2025) and crop [...] Read more.

This study examines the multi-scale relationships between extreme climate indices and maize yield from a hydrothermal perspective, across both temporal (long-term trends, interannual anomalies, and abrupt changes) and spatial (regional and grid) scales in the Chengdu–Chongqing region, using long-term meteorological (1985–2025) and crop yield (1982–2015) datasets. Results reveal pronounced warming and drying trends, characterized by increasing warm-related temperature extremes and consecutive dry days, along with a decline in cold extremes. A shift toward drier conditions occurred around 2005, while temperature extremes have exhibited stepwise changes since the late 1990s. Maize yield shows a significant upward trend with an abrupt increase around 1997, closely linked to reduced cold stress. Scale-dependent analyses reveal that climate-yield relationships are primarily expressed through long-term hydrothermal changes rather than short-term variability, with maize yield showing positive responses to warm conditions and prolonged dry spell duration, and negative responses to cold extremes and excessive precipitation. In contrast, relationships based on interannual anomalies are weak and spatially inconsistent, suggesting limited sensitivity of yield to short-term climate variability due to system buffering and agricultural adaptation. Spatially, climate–yield relationships exhibit marked heterogeneity, with temperature constraints dominating in the western region and moisture-related effects being more pronounced in the central–eastern basin. Mechanistically, abrupt change analysis indicates two distinct controls: cold extremes act as threshold constraints associated with rapid yield shifts, whereas warming and drying exert gradual cumulative effects on productivity. Overall, maize yield dynamics are more strongly associated with long-term hydrothermal evolution than interannual variability, highlighting the importance of distinguishing temporal scales, hydrothermal regimes and long-term agricultural system evolution in climate–crop assessments under ongoing climate change. Full article

(This article belongs to the Section Climatology)

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19 pages, 2605 KB

Open AccessArticle

PM_2.5 Prediction Based on LSTM Weighted by K-Nearest Neighbor Algorithm

by Lili Wang, Zhengwu Hu and Zuhan Liu

Atmosphere 2026, 17(6), 585; https://doi.org/10.3390/atmos17060585 - 5 Jun 2026

Accurate prediction of PM_2.5 concentration is essential for public health and environmental protection, and specifically crucial for the management of the availability of sufficient health personnel during adverse health episodes. However, its nonlinearity, variability, and complexity make this task challenging. This study [...] Read more.

Accurate prediction of PM_2.5 concentration is essential for public health and environmental protection, and specifically crucial for the management of the availability of sufficient health personnel during adverse health episodes. However, its nonlinearity, variability, and complexity make this task challenging. This study proposes a long short-term memory (LSTM) weighted by K-nearest neighbor (KNN) algorithm (namely Weighted KNN-LSTM Model) that can effectively predict the PM_2.5 concentration time series. Firstly, the K-nearest neighbors of each time point are sought based on the Euclidean distance within the data time range. Given that neighboring observations typically exert a more pronounced influence than distant ones in spatial processes, weights are accordingly assigned to these neighbors to quantitatively reflect their relative importance in the analysis. Subsequently, after the initial data is processed by the weighted KNN algorithm, it is reorganized and transformed into a reconstructed dataset with a size K times that of the original data. The data used for model training and the data used for evaluating the model’s prediction performance are completely independent, and the test dataset is never involved in the model training process to ensure the authenticity and reliability of the prediction performance evaluation. Then, the LSTM neural network model is trained on this new dataset to enhance its generalization ability. The experimental results show that the weighted KNN-LSTM model exhibits excellent predictive performance in predicting PM_2.5 concentration. It is important to note that the dataset used to evaluate the model’s performance was strictly independent from the data used to train the model. This separation ensures that the reported accuracy reflects true predictive capability rather than mere fitting quality. The model provides a technical reference for hourly PM_2.5 concentration prediction in Nanchang City, and the prediction results can be used as an auxiliary reference for regional air quality monitoring; the application of the model in heavy pollution warnings needs to be further optimized and verified by combining multi-source data such as meteorology, which provide reliable data support for the formulation of dynamic emission reduction policies. Full article

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

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21 pages, 2078 KB

Open AccessArticle

Quantifying Meteorological and Emission-Control Contributions to PM_2.5 and Ozone Changes During the 2023 G20 Summit in New Delhi

by Zhiwei Han, Chenliang Tao, Mengyuan Zhang, Shuhuan Wang, Ying Chen and Hongliang Zhang

Atmosphere 2026, 17(6), 584; https://doi.org/10.3390/atmos17060584 - 5 Jun 2026

India faces severe PM_2.5–O₃ compound pollution, and the 2023 G20 Summit in New Delhi provided a valuable case for examining how short-term emission controls interact with unfavorable late-monsoon meteorology. In this study, the WRF-CMAQ modeling system was applied to quantify [...] Read more.

India faces severe PM_2.5–O₃ compound pollution, and the 2023 G20 Summit in New Delhi provided a valuable case for examining how short-term emission controls interact with unfavorable late-monsoon meteorology. In this study, the WRF-CMAQ modeling system was applied to quantify the relative contributions of meteorological variability and graded multisectoral emission controls to PM_2.5 and ozone changes during the summit period. The results show that both pollutants exhibited clear stage-dependent variations, with lower concentrations during the summit and rapid rebound afterward. Relative to the 2022 meteorology sensitivity case, the 2023 meteorological background increased PM_2.5 by 6.76 μg/m³ and MDA8 O₃ by 4.37 ppb over New Delhi, indicating a distinct meteorological penalty during the monsoon withdrawal period. Under progressively strengthened control scenarios, PM_2.5 declined from 79.01 to 66.35 μg/m³, while MDA8 O₃ decreased from 81.19 to 77.67 ppb. The strongest control scenario reduced PM_2.5 more than the meteorological penalty and substantially mitigated the ozone enhancement, although it did not fully offset the adverse meteorological effect on O₃. These findings demonstrate that high-intensity coordinated controls can effectively alleviate PM_2.5–O₃ compound pollution even under unfavorable meteorological conditions. Full article

(This article belongs to the Special Issue Global and Regional Perspectives on Particulate Matter and Air Quality: Environmental and Health Impacts, Challenges, Policies, and Solutions)

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20 pages, 5661 KB

Open AccessArticle

Assessment of Snow Cover Contamination in Pavlodar, Kazakhstan, Based on Elemental Analysis and Pollution Indices

by Zhadyranova Aliya, Baigazinov Zhanat, Aliyev Nursultan, Mukhamediyarov Nurlan, Zhumadilov Kassym, Polivkina Yelena, Salmenbayev Sayan and Aktayev Medet

Atmosphere 2026, 17(6), 583; https://doi.org/10.3390/atmos17060583 - 5 Jun 2026

Seasonal snow cover can serve as an informative single-season indicator of atmospheric deposition in industrial urban areas because it accumulates airborne contaminants during the winter period. A total of 55 snow samples were collected across the urban area, and the liquid phase was [...] Read more.

Seasonal snow cover can serve as an informative single-season indicator of atmospheric deposition in industrial urban areas because it accumulates airborne contaminants during the winter period. A total of 55 snow samples were collected across the urban area, and the liquid phase was analyzed for major and trace elements using instrumental elemental analysis with defined detection limits and measurement uncertainty. Descriptive statistics, background comparisons, and integrated pollution indicators were used to characterize the spatial variability and intensity of contamination. The results showed that the median concentrations of most analyzed elements did not exceed the reference limits; however, aluminum and iron exhibited elevated levels, with aluminum reaching 1.1–27 times and iron 1.0–3 times the reference values. Median concentrations included 270 μg L⁻¹ for Al, 118 μg L⁻¹ for Fe, 30 μg L⁻¹ for Zn, 11.5 μg L⁻¹ for Ni, and 7.3 μg L⁻¹ for Pb. The obtained data indicate a heterogeneous pollution pattern across Pavlodar and suggest the combined influence of mineral dust, urban-industrial emissions, road-dust resuspension, and natural inputs on snow chemistry. Because the study is based on one winter sampling campaign, the results should be interpreted as a single-season assessment of snow-cover contamination rather than as evidence of long-term temporal stability. Full article

(This article belongs to the Special Issue Advances in Air Quality Monitoring and Source Apportionment)

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15 pages, 2310 KB

Open AccessArticle

VOC Emission Idle Rates and Differentiated Control Strategies for Chemical Enterprises Under China’s Discharge Permit System: Evidence from Jiangsu Province

by Xuemei Liu, Xiufang Zhu, Jianfeng Pang and Xijun Ma

Atmosphere 2026, 17(6), 582; https://doi.org/10.3390/atmos17060582 - 4 Jun 2026

China’s pollutant discharge permit system mandates total-quantity emission control for industrial volatile organic compounds (VOCs), yet the actual utilization of permitted capacity remains poorly studied. This study developed an “emission idle rate” (IR = 1 − actual/permitted emissions) framework and applied it to [...] Read more.

China’s pollutant discharge permit system mandates total-quantity emission control for industrial volatile organic compounds (VOCs), yet the actual utilization of permitted capacity remains poorly studied. This study developed an “emission idle rate” (IR = 1 − actual/permitted emissions) framework and applied it to 130 chemical enterprises across three cities in Jiangsu Province using 2020–2024 panel data. The mean idle rate reached 78.1%, with no significant inter-city differences (H = 0.96, p = 0.619), attributable to both production underutilization and systematic over-estimation of emission ceilings inherent in the design-capacity-based permit methodology. Ward hierarchical clustering revealed three emission behavioral patterns, Persistent Surplus (n = 74, IR = 0.95), Declining Surplus (n = 32, IR = 0.69), and Growing Surplus (n = 19, IR = 0.59), exhibiting distinct idle rate levels and temporal trajectories. Cluster differentiation was significantly associated only with production-side emission characteristics, while enterprise economic variables showed no significant effects. The estimated tradeable emission surplus reached 668.3 t/a, though its realization faces transaction cost barriers including the lack of standardized transfer mechanisms and formal VOC trading infrastructure. A quadrant-based strategy matrix integrating idle rate levels with temporal trends is proposed for differentiated permit management. Full article

(This article belongs to the Section Air Pollution Control)

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16 pages, 11844 KB

Open AccessArticle

Spectral Characteristics of VLF Transmitter Amplitude Variations During Sunrise Under Solar Minimum Conditions

by Jorge Samanes and Ricardo Y. C. Cueva

Atmosphere 2026, 17(6), 581; https://doi.org/10.3390/atmos17060581 - 4 Jun 2026

Very low frequency (VLF) radio waves propagating within the Earth–ionosphere waveguide are highly sensitive to changes in lower ionospheric conditions, which are reflected in the amplitude of received transmitter signals. During the solar terminator passage, rapid changes in ionospheric conductivity modify propagation conditions [...] Read more.

Very low frequency (VLF) radio waves propagating within the Earth–ionosphere waveguide are highly sensitive to changes in lower ionospheric conditions, which are reflected in the amplitude of received transmitter signals. During the solar terminator passage, rapid changes in ionospheric conductivity modify propagation conditions and produce characteristic VLF amplitude minima associated with modal interference and mode conversion processes. In this study, we investigate the spectral characteristics of VLF amplitude variability during the sunrise transition, which spans extended time intervals along long west–east propagation paths, using signals from the NPM-PIU and NPM-PLO paths recorded in Peru under solar minimum conditions (2008–2010). One-hour intervals centered on amplitude minima are analyzed using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) combined with the continuous wavelet transform. The analysis reveals recurrent wave-like fluctuations (WFs) with dominant periods between 2 and 6 min, whose amplitudes increase systematically within ±15 min around the amplitude minima. These fluctuations are better distinguished during the later-stage minima and exhibit enhanced occurrence during solstice months. The results indicate that the evolving modal structure of the waveguide during the sunrise transition may enhance the sensitivity of the VLF signals to small perturbations, enabling the detection of weak short-period ionospheric disturbances. Full article

(This article belongs to the Special Issue Atmospheric Impacts of Space Weather and Extreme Meteorological Events)

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22 pages, 7024 KB

Open AccessArticle

Numerical Simulation of the Diurnal Cycle of the West Texas Dryline: Impacts of Topography and Surface Moisture

by Duanjun Lu and Loren D. White

Atmosphere 2026, 17(6), 580; https://doi.org/10.3390/atmos17060580 - 3 Jun 2026

The dryline is a sharp boundary between moist air from the Gulf of Mexico and dry air from the desert Southwest. In West Texas, this boundary often surges east during the day and retreats west at night. Understanding exactly why it moves back [...] Read more.

The dryline is a sharp boundary between moist air from the Gulf of Mexico and dry air from the desert Southwest. In West Texas, this boundary often surges east during the day and retreats west at night. Understanding exactly why it moves back and forth is critical for predicting where severe thunderstorms will form. Yet the physical drivers of dryline life cycle remain poorly understood and frequently under-predicted. This study utilizes a variable-resolution Model for Prediction Across Scales (MPAS) configuration (3–60 km) with the YSU non-local planetary boundary layer (PBL) scheme to investigate a representative dryline event from April 2017. The control simulation was validated against NWS Surface Analysis, demonstrating a high spatial correlation in both synoptic-scale pressure distributions and mesoscale moisture gradients, successfully resolving a nocturnal retrogression of approximately 170 km, with the dryline retreating from its peak afternoon surge at 100.7° W to a recovery point of 102.5° W between 0000 UTC and 0600 UTC 10 April. This recovery occurred at an average speed of 28.3 km/h, consistently constrained beneath a resilient capping inversion. To decouple the environmental drivers of this motion, two targeted sensitivity experiments were conducted: (1) Mechanical Forcing: A 50% reduction in regional topography confirms that the West Texas sloping ramp acts as a “topographic pump.” Without this gradient, the hydrostatic pressure falls were insufficient to drive the nocturnal retreat, causing the boundary to stall eastward. (2) Thermodynamic Regulation: A 50% reduction in soil moisture revealed an “energy swap,” the near-total partitioning of net radiation into sensible heat drove the planetary boundary layer to a higher peak value—a 600 m increase over the control simulation. These results provide a comprehensive physical framework for dryline mobility, demonstrating that while terrain plays an important role in the extent of the diurnal oscillation, soil moisture governs the vertical structure and moisture gradient intensity. Our findings suggest that high-resolution vertical layering and accurate land-surface initialization are prerequisites for capturing the inversion layer dynamics essential for dryline forecasting. However, these findings are based on a single event and require validation across a broader range of dryline cases. Full article

(This article belongs to the Section Meteorology)

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20 pages, 11313 KB

Open AccessArticle

Rainfall Variability in the Brazilian Subtropical Climate Associated with El Niño–Southern Oscillation Diversity

by Gabriela Goudard, Leila Limberger, Camila Bertoletti Carpenedo and Francisco Mendonça

Atmosphere 2026, 17(6), 579; https://doi.org/10.3390/atmos17060579 - 3 Jun 2026

The El Niño–Southern Oscillation (ENSO) is the main driver of interannual climate variability, strongly influencing precipitation, temperature, and extreme events worldwide. In South America, its impacts are well documented. However, studies examining different ENSO types—Eastern Pacific (EP), Central Pacific (CP), and Mixed (MX), [...] Read more.

The El Niño–Southern Oscillation (ENSO) is the main driver of interannual climate variability, strongly influencing precipitation, temperature, and extreme events worldwide. In South America, its impacts are well documented. However, studies examining different ENSO types—Eastern Pacific (EP), Central Pacific (CP), and Mixed (MX), defined according to the location of sea surface temperature (SST) anomalies in the tropical Pacific—remain limited, particularly for the Brazilian subtropical climate. This study investigates rainfall variability in the Brazilian subtropical region associated with different ENSO types. Composite analyses of precipitation, wind, and SST anomalies were performed, and monthly rainfall data from 703 stations were used to identify homogeneous regions. The results show the intensity and spatial coherence of rainfall signals vary according to El Niño type, with EP events favoring widespread wet conditions and CP events producing more heterogeneous or locally negative anomalies. For La Niña, the intensity and seasonal distribution of negative rainfall anomalies vary by ENSO type: stronger impacts occur in summer (EP), spring (MX), and autumn (CP). These findings improve the understanding of ENSO-related rainfall variability in the Brazilian subtropical region and provide valuable insights for the management of climate-related risks in an area frequently affected by rainfall extremes. Full article

(This article belongs to the Special Issue Research on ENSO: Types and Impacts)

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18 pages, 1956 KB

Open AccessArticle

Atmospheric Aging of Organic Carbon and Polycyclic Aromatic Compounds Emitted from Residential Solid Fuel Combustion: Effects of Fuel Type and Combustion Temperature

by Yuwei Liu, Yu Peng, Yanjie Lu and Yingjun Chen

Atmosphere 2026, 17(6), 578; https://doi.org/10.3390/atmos17060578 - 3 Jun 2026

Residential solid fuels are widely used for cooking and heating, but the atmospheric evolution of their particulate emissions remains insufficiently characterized. To address this gap, we constructed an integrated quartz-tube furnace–dilution–oxidation flow reactor (OFR) system for direct comparison of fresh and OFR-aged emissions [...] Read more.

Residential solid fuels are widely used for cooking and heating, but the atmospheric evolution of their particulate emissions remains insufficiently characterized. To address this gap, we constructed an integrated quartz-tube furnace–dilution–oxidation flow reactor (OFR) system for direct comparison of fresh and OFR-aged emissions across fuel types and combustion temperatures. Six biomass fuels and six coals were burned at 500 °C and 800 °C. Organic carbon (OC) subfractions and polycyclic aromatic compounds (PACs), including 16 parent polycyclic aromatic hydrocarbons (pPAHs) and 9 oxygenated polycyclic aromatic hydrocarbons (oPAHs), were quantified. In fresh emissions, increasing temperature reduced OC emission factors for both fuel types, whereas PAC emission factors increased for biomass but decreased for coal. OFR aging generally increased particulate OC and shifted OC toward less volatile or more thermally stable fractions. For coal burned at 500 °C, pPAHs decreased by 64%, whereas oPAHs increased by 127%. Although the overall quantitative structure–activity relationship (QSAR)-derived carcinogenicity indicator of PACs decreased by 46%, the oPAH contribution increased from 7% to 18%. These findings show that metrics based only on fresh emissions cannot fully capture the chemical evolution and toxicity-related implications of residential solid fuel emissions. Full article

(This article belongs to the Section Air Pollution Control)

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27 pages, 5835 KB

Open AccessArticle

Long-Term Volcanic Signal in 21st-Century Climate Projections with a 25-Member Stochastic Ensemble Using SOCOL-MPIOM

by Margarita A. Tkachenko and Eugene V. Rozanov

Atmosphere 2026, 17(6), 577; https://doi.org/10.3390/atmos17060577 - 2 Jun 2026

Future volcanic eruptions are largely omitted from CMIP6 simulations, thereby increasing the uncertainty in 21st-century climate projections. We performed an 80-year (2020–2100) 25-member stochastic ensemble simulation with the climate model SOCOL-MPIOM, driven by the SSP3-7.0 forcing scenario, and introduced five stochastically distributed tropical [...] Read more.

Future volcanic eruptions are largely omitted from CMIP6 simulations, thereby increasing the uncertainty in 21st-century climate projections. We performed an 80-year (2020–2100) 25-member stochastic ensemble simulation with the climate model SOCOL-MPIOM, driven by the SSP3-7.0 forcing scenario, and introduced five stochastically distributed tropical eruptions—three strong, one moderate, and one weak—for each ensemble member (hereafter, SV run). We analyse volcanic influence by comparing the SV run results against a single volcanic-free baseline simulation under the same anthropogenic forcing scenario. Five eruptions over the 80-year simulation period leave the trends of the major climate indicators statistically indistinguishable from those of the volcanic-free baseline at the global and annual mean scales. However, on local and seasonal scales, volcanic activity can substantially alter the results of the volcanic-free simulation. For example, over Northern Europe, volcanic eruptions produce winter temperature warming of up to 1.0 K (about 30% of the warming in the reference run) and an annual precipitation deficit of 36 mm yr⁻¹. This emphasises the need to include volcanic eruptions for more accurate projections of future climate. Probabilistic analysis of the SV ensemble shows that the annual maximum daily temperature (TXx) exceeds +0.5 K over 16% of global land with more-likely-than-not probability, a perturbation absent in standard CMIP6 results. Since our scenario composition targets the upper bound of plausible 21st-century volcanic activity, these exceedance areas represent near-maximum rather than most-probable estimates. Full article

(This article belongs to the Section Climatology)

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