Atmosphere

17 pages, 3396 KB

Open AccessArticle

Determinants of Odor-Related Perception: Analysis of Community Response

by Franciele Ribeiro Cavalcante, Milena Machado, Valdério Anselmo Reisen, Bruno Furieri, Elisa Valentim Goulart, Antonio Ponce de Leon, Neyval Costa Reis, Jr., Séverine Frère and Jane Meri Santos

Atmosphere 2025, 16(10), 1176; https://doi.org/10.3390/atmos16101176 (registering DOI) - 11 Oct 2025

Abstract

This study intends to identify and quantify the individual, perceptual, and contextual factors associated with odor-related perception and to assess the perception of odor sources according to meteorological conditions. Two face-to-face seasonal community surveys were conducted using stratified random sampling with proportional allocation, [...] Read more.

This study intends to identify and quantify the individual, perceptual, and contextual factors associated with odor-related perception and to assess the perception of odor sources according to meteorological conditions. Two face-to-face seasonal community surveys were conducted using stratified random sampling with proportional allocation, yielding representative samples of residents in a southern Brazilian city, where mild constant temperatures throughout the year and shifting prevailing wind directions expose residents to different odor sources. Chi-Square tests were applied to assess associations between odor perception and qualitative variables, while logistic regression was used to identify predictors of higher annoyance. Results showed that prevailing wind direction influenced source attribution, with steel industry and sewage-related sites most frequently cited. Proximity to the steel plant increased both source recognition and annoyance levels. Reported impacts included closing windows and reducing outdoor activities. Self-reported respiratory problems consistently predicted higher annoyance levels in both surveys. The statistical methods were effective in analyzing the likelihood of odor-related perception and its relationship with explanatory variables. These findings highlight the value of a data-driven approach—specifically, integrating wind direction, source proximity, and community-based perception—to support urban environmental management and guide odor mitigation strategies. Full article

(This article belongs to the Special Issue Atmospheric Pollutants: Monitoring and Observation (2nd Edition))

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14 pages, 4878 KB

Open AccessArticle

Near-Surface Temperature Prediction Based on Dual-Attention-BiLSTM

by Wentao Xie, Mei Du, Chengbo Li and Guangxin Du

Atmosphere 2025, 16(10), 1175; https://doi.org/10.3390/atmos16101175 - 10 Oct 2025

Abstract

Current temperature prediction methods often focus on time-series information while neglecting the contributions of different meteorological factors and the context of varying time steps. Accordingly, this study developed a Dual-Attention-BiLSTM (a bidirectional long short-term memory network with dual attention mechanisms) network model, which [...] Read more.

Current temperature prediction methods often focus on time-series information while neglecting the contributions of different meteorological factors and the context of varying time steps. Accordingly, this study developed a Dual-Attention-BiLSTM (a bidirectional long short-term memory network with dual attention mechanisms) network model, which integrates a bidirectional long short-term memory (BiLSTM) network model with random forest-based feature selection and two self-designed attention mechanisms. A sensitivity analysis was conducted to evaluate the influence of the attention mechanisms. This study focuses on Shijiazhuang City, China, which has a temperate continental monsoon climate with significant seasonal and daily variations. The data were sourced from ERA5-Land, comprising hourly near-surface temperature and related meteorological variables for the year of 2022. The results indicate that integrating the two attention mechanisms significantly improves the model’s prediction performance compared to using BiLSTM alone. The mean absolute error between simulation results ranges from 0.80 °C to 1.08 °C, with a reduction of 0.17 °C to 0.39 °C, and the root mean square error ranges from 1.17 °C to 1.37 °C, with a reduction of 0.12 °C to 0.22 °C. Full article

(This article belongs to the Section Meteorology)

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21 pages, 5696 KB

Open AccessReview

Advancing Research on Urban Ecological Corridors in the Context of Carbon Neutrality: Insights from Bibliometric and Systematic Reviews

by Jing Li, Lang Zhang, Yang Yi and Jingbo Hong

Atmosphere 2025, 16(10), 1174; https://doi.org/10.3390/atmos16101174 - 10 Oct 2025

Abstract

The construction and maintenance of ecological corridors not only facilitate species migration and gene flow but also enhance ecosystem stability and resilience, providing critical support for achieving global carbon neutrality goals. Despite their importance, research on urban ecological corridors—specifically their role in carbon [...] Read more.

The construction and maintenance of ecological corridors not only facilitate species migration and gene flow but also enhance ecosystem stability and resilience, providing critical support for achieving global carbon neutrality goals. Despite their importance, research on urban ecological corridors—specifically their role in carbon sequestration and emission reduction within urban environments—remains insufficiently explored. To address this gap, we employed bibliometric and network analysis methods, utilizing the CiteSpace6.3.1 visualization tool to systematically review existing literature from the Web of Science Core Collection database. This study examines the research progress and trends in urban ecological corridors from 2000 to 2023, focusing on their role and significance in the context of global carbon neutrality. The findings reveal the following: (1) Research attention has grown steadily from 2000 to 2023, with climate change, carbon emission dynamics, and biodiversity emerging as core themes, reflecting increasing global focus on the carbon neutrality functions of urban ecological corridors. (2) CiteSpace analysis identified key research hotspots through keywords including climate change, carbon cycle, ecosystem services, model simulation, and ecological network analysis, revealing the functional mechanisms and pathways of urban ecological corridors in carbon neutrality contexts. (3) Current scientific challenges focus on understanding three core aspects of urban ecological corridors, the compositional elements, spatial structural design, and functional capacity assessment, requiring systematic theoretical breakthroughs. (4) Future research should prioritize exploring mechanisms to enhance urban ecological corridor functions and constructing low-carbon urban ecological networks, providing theoretical guidance and practical pathways for achieving urban emission reduction and climate goals. This study contributes to integrating research on the effectiveness of urban ecological corridors and carbon sinks, offering theoretical insights and practical guidance for reducing urban emissions and achieving climate goals. Full article

(This article belongs to the Special Issue Water Resource Challenges and Sustainable Management Solutions Under the Interaction of Climate Change and Human Activities)

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23 pages, 15077 KB

Open AccessArticle

Landscape Patterns and Carbon Emissions in the Yangtze River Basin: Insights from Ensemble Models and Nighttime Light Data

by Banglong Pan, Qi Wang, Zhuo Diao, Jiayi Li, Wuyiming Liu, Qianfeng Gao, Ying Shu and Juan Du

Atmosphere 2025, 16(10), 1173; https://doi.org/10.3390/atmos16101173 - 9 Oct 2025

Abstract

Land use patterns are a critical driver of changes in carbon emissions, making it essential to elucidate the relationship between regional carbon emissions and land use types. As a nationally designated economic strategic zone, the Yangtze River Basin encompasses megacities, rapidly developing medium-sized [...] Read more.

Land use patterns are a critical driver of changes in carbon emissions, making it essential to elucidate the relationship between regional carbon emissions and land use types. As a nationally designated economic strategic zone, the Yangtze River Basin encompasses megacities, rapidly developing medium-sized cities, and relatively underdeveloped regions. However, the mechanism underlying the interaction between landscape patterns and carbon emissions across such gradients remains inadequately understood. This study utilizes nighttime light, land use and carbon emissions datasets, employing XGBoost, CatBoost, LightGBM and a stacking ensemble model to analyze the impacts and driving factors of land use changes on carbon emissions in the Yangtze River Basin from 2002 to 2022. The results showed: (1) The stacking ensemble learning model demonstrated the best predictive performance, with a coefficient of determination (R²) of 0.80, a residual prediction deviation (RPD) of 2.22, and a root mean square error (RMSE) of 4.46. Compared with the next-best models, these performance metrics represent improvements of 19.40% in R² and 28.32% in RPD, and a 22.16% reduction in RMSE. (2) Based on SHAP feature importance and Pearson correlation analysis, the primary drivers influencing CO₂ net emissions in the Yangtze River Basin are GDP per capita (GDPpc), population density (POD), Tertiary industry share (TI), land use degree comprehensive index (LUI), dynamic degree of water-body land use (K_water), Largest patch index (LPI), and number of patches (NP). These findings indicate that changes in regional landscape patterns exert a significant effect on carbon emissions in strategic economic regions, and that stacked ensemble models can effectively simulate and interpret this relationship with high predictive accuracy, thereby providing decision support for regional low-carbon development planning. Full article

(This article belongs to the Special Issue Urban Carbon Emissions: Measurement and Modeling)

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17 pages, 7446 KB

Open AccessArticle

Seasonal Cycle of the Total Ozone Content over Southern High Latitudes in the CCM SOCOLv3

by Anastasia Imanova, Tatiana Egorova, Vladimir Zubov, Andrey Mironov, Alexander Polyakov, Georgiy Nerobelov and Eugene Rozanov

Atmosphere 2025, 16(10), 1172; https://doi.org/10.3390/atmos16101172 - 9 Oct 2025

Abstract

The severe ozone depletion over the Southern polar region, known as the “ozone hole,” is a stark example of global ozone depletion caused by human-made chemicals. This has implications for climate change and increased harmful surface solar UV. Several Chemistry–Climate models (CCMs) tend [...] Read more.

The severe ozone depletion over the Southern polar region, known as the “ozone hole,” is a stark example of global ozone depletion caused by human-made chemicals. This has implications for climate change and increased harmful surface solar UV. Several Chemistry–Climate models (CCMs) tend to underestimate total column ozone (TCO) against satellite measurements over the Southern polar region. This underestimation can reach up to 50% in monthly mean zonally averaged biases during cold seasons. The most significant discrepancies were found in the CCM SOlar Climate Ozone Links version 3 (SOCOLv3). We use SOCOLv3 to study the sensitivity of Antarctic TCO to three key factors: (1) stratospheric heterogeneous reaction efficiency, (2) meridional flux intensity into polar regions from sub-grid scale mixing, and (3) photodissociation rate calculation accuracy. We compared the model results with satellite data from Infrared Fourier Spectrometer-2 (IKFS-2), Microwave Limb Sounder (MLS), and Michelson Interferometer for Passive Atmospheric Sounding (MIPAS). The most effective processes for improving polar ozone simulation are photolysis and horizontal mixing. Increasing horizontal mixing improves the simulated TCO seasonal cycle but negatively impacts CH₄ and N₂O distributions. Using the Cloud-J v.8.0 photolysis module has improved photolysis rate calculations and the seasonal ozone cycle representation over the Southern polar region. This paper outlines how different processes impact chemistry–climate model performance in the southern polar stratosphere, with potential implications for future advancements. Full article

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

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26 pages, 4574 KB

Open AccessReview

Assessment of Climate Vulnerability Indices for Coastal Tourism Destinations

by Beatriz Gasalla-López, Manuel Arcila-Garrido and Juan Adolfo Chica-Ruiz

Atmosphere 2025, 16(10), 1171; https://doi.org/10.3390/atmos16101171 - 9 Oct 2025

Abstract

Coastal ecosystems are crucial for territorial development but they face increasing pressure from population growth and climate change. These factors threaten ecosystems, communities, and tourism infrastructure. It is essential to assess vulnerability to achieve adaptation and indices are widely used for this purpose [...] Read more.

Coastal ecosystems are crucial for territorial development but they face increasing pressure from population growth and climate change. These factors threaten ecosystems, communities, and tourism infrastructure. It is essential to assess vulnerability to achieve adaptation and indices are widely used for this purpose due to their simplicity. However, inconsistencies persist in definitions, methodologies, dimensions, and variable selection. This systematic review of 43 second-generation studies analyzes the evolution of conceptual approaches, identifies the most common indicators, and examines index methodologies. The results reveal that, although the IPCC has updated its definition of vulnerability, many publications still use previous conceptual frameworks. While temperature is relevant to tourism, most studies focus on increasing sea level and its effects. In some cases, social and economic dimensions are treated jointly whereas in other studies they are considered separately. Variable selection remains case-specific and a robust, standardized framework is still lacking, especially for social aspects. Despite the undoubted importance of tourism, specific research on this sector is scarce. This review underscores the need for standardized indices tailored to coastal tourism management under climate change. Future research directions are also proposed. Full article

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

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24 pages, 2315 KB

Open AccessArticle

Mitigating Climate Warming: Mechanisms and Actions

by Jianhui Bai, Xiaowei Wan, Angelo Lupi, Xuemei Zong and Erhan Arslan

Atmosphere 2025, 16(10), 1170; https://doi.org/10.3390/atmos16101170 - 9 Oct 2025

Abstract

To validate a positive relationship between air temperature (T) and atmospheric substances (S/G, a ratio of diffuse solar radiation to global solar radiation) found at four typical stations on the Earth, and a further investigation was conducted. Based on the analysis of long-term [...] Read more.

To validate a positive relationship between air temperature (T) and atmospheric substances (S/G, a ratio of diffuse solar radiation to global solar radiation) found at four typical stations on the Earth, and a further investigation was conducted. Based on the analysis of long-term solar radiation, atmospheric substances, and air temperature at 29 representative stations of baseline surface radiation network (BSRN) in the world, the relationships and the mechanisms between air temperature and atmospheric substances were studied in more detail. A universal non-linear relationship between T and S/G was still found, which supported the previous relationship between T and S/G. This further revealed that a high (or low) air temperature is strongly associated with large (or small) amounts of atmospheric substances. The mechanism is that all kinds of atmospheric substances can keep and accumulate solar energy in the atmosphere and then heat the atmosphere, causing atmospheric warming at the regional and global scales. Therefore, it is suggested to reduce the direct emissions of all kinds of atmospheric substances (in terms gases, liquids and particles, and GLPs) from the natural and anthropogenic sources, and secondary formations produced from atmospheric compositions via chemical and photochemical reactions (CPRs) in the atmosphere, to slow down the regional and global warming through our collective efforts, by all mankind and all nations. Air temperature increased at most BSRN stations and many sites in China, and decreased at a small number of BSRN stations during long time scales, revealing that the mechanisms of air temperature change were very complex and varied with region, atmospheric substances, and the interactions between solar radiation, GLPs, and the land. Full article

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

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12 pages, 1521 KB

Open AccessArticle

Investigation and Analysis of Indoor Radon Concentrations in Typical Residential Areas in Central China

by Cong Li, Jun Deng, Gangtao Sun, Fang Wang, Jie Yu, Qi Xiao, Shi Liu and Wenshan Zhou

Atmosphere 2025, 16(10), 1169; https://doi.org/10.3390/atmos16101169 - 9 Oct 2025

Abstract

In recent years, China has experienced a notable increase in indoor radon concentrations. However, our understanding of residential radon exposure in Central China remains limited and primarily depends on the data collected from residential buildings in Wuhan before 2003. Given this context, the [...] Read more.

In recent years, China has experienced a notable increase in indoor radon concentrations. However, our understanding of residential radon exposure in Central China remains limited and primarily depends on the data collected from residential buildings in Wuhan before 2003. Given this context, the current radon exposure levels in Central China must be assessed immediately, and the factors influencing them be investigated. To address this gap, our study focused on five representative areas in Central China. We monitored indoor radon concentrations in residential areas using random cluster sampling while considering various building structures. The radon levels were measured through the alpha track method, and RSKS standard detectors were deployed in two separate batches to participating households. A total of 1300 detectors were distributed across 579 households, with a recovery rate of 97.15% (1263 detectors were retrieved). The annual average indoor radon concentration in Central China ranged widely from 6.25 Bq/m³ to 310.44 Bq/m³, with an arithmetic mean of 50.20 Bq/m³, which resulted in an average annual effective dose of 2.08 mSv. Referring to World Health Organization standards, the radon concentration in approximately 8.24% of the monitored rooms exceeded the recommended action level. Our analysis indicated that radon concentration is primarily influenced by factors, such as the time of measurement, geographical location, building structure, floor materials, household fuel, and ventilation practices. Multiple regression analysis revealed that these factors collectively account for 10.80% of the variation in radon concentration. Notably, geographical location, building structure, and ventilation mode emerged as important factors. Based on these findings, our study suggests several practical measures to effectively reduce indoor radon levels, including improving ventilation, switching to cleaner fuels, and using environmentally friendly building and decoration materials. Full article

(This article belongs to the Special Issue Environmental Radon Measurement and Radiation Exposure Assessment)

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19 pages, 7633 KB

Open AccessArticle

A Transfer Learning–CNN Framework for Marine Atmospheric Pollutant Inversion Using Multi-Source Data Fusion

by Xiaoling Li, Xiaoyu Liu, Xiaohuan Liu, Zhengyang Zhu, Yunhui Xiong, Jingfei Hu and Xiang Gong

Atmosphere 2025, 16(10), 1168; https://doi.org/10.3390/atmos16101168 - 8 Oct 2025

Abstract

The concentration characteristics of SO₂, NO₂, O₃, and CO in the marine atmosphere are of great significance for understanding air–sea interactions and regional atmospheric chemical processes. However, due to the challenging conditions of marine monitoring, long-term continuous [...] Read more.

The concentration characteristics of SO₂, NO₂, O₃, and CO in the marine atmosphere are of great significance for understanding air–sea interactions and regional atmospheric chemical processes. However, due to the challenging conditions of marine monitoring, long-term continuous observational data remain scarce. To address this gap, this study proposes a Transfer Learning–Convolutional Neural Network (TL-CNN) model that integrates ERA5 meteorological data, EAC4 atmospheric composition reanalysis data, and ground-based observations through multi-source data fusion. During data preprocessing, the Data Interpolating Empirical Orthogonal Function (DINEOF), inverse distance weighting (IDW) spatial interpolation, and Gaussian filtering methods were employed to improve data continuity and consistency. Using ERA5 meteorological variables as inputs and EAC4 pollutant concentrations as training targets, a CNN-based inversion framework was constructed. Results show that the CNN model achieved an average coefficient of determination (R²) exceeding 0.80 on the pretraining test set, significantly outperforming random forest and deep neural networks, particularly in reproducing nearshore gradients and regional spatial distributions. After incorporating transfer learning and fine-tuning with station observations, the model inversion results reached an average R² of 0.72 against site measurements, effectively correcting systematic biases in the reanalysis data. Among the pollutants, the inversion of SO₂ performed relatively poorly, mainly because emission reduction trends from anthropogenic sources were not sufficiently represented in the reanalysis dataset. Overall, the TL-CNN model provides more accurate pollutant concentration fields for offshore regions with limited observations, offering strong support for marine atmospheric environment studies and assessments of marine ecological effects. It also demonstrates the potential of combining deep learning and transfer learning in atmospheric chemistry research. Full article

(This article belongs to the Section Aerosols)

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13 pages, 1968 KB

Open AccessArticle

Assessing the Annual-Scale Insolation–Temperature Relationship over Northern Hemisphere in CMIP6 Models and Its Implication for Orbital-Scale Simulation

by Shengmei Li and Jian Shi

Atmosphere 2025, 16(10), 1167; https://doi.org/10.3390/atmos16101167 - 8 Oct 2025

Abstract

Previous studies have suggested that Earth’s annual cycle of modern climate provides information relevant to orbital-scale climate variability, since both are driven by solar insolation changes determined by orbital geometry. However, there has been no systematic assessment of the climate response to annual-scale [...] Read more.

Previous studies have suggested that Earth’s annual cycle of modern climate provides information relevant to orbital-scale climate variability, since both are driven by solar insolation changes determined by orbital geometry. However, there has been no systematic assessment of the climate response to annual-scale insolation changes in climate models, leading to large uncertainty in orbital-scale simulation. In this study, we evaluate the Northern Hemisphere land surface air temperature response to the annual insolation cycle in the Coupled Model Intercomparison Project Phase 6 (CMIP6) models. A polynomial transfer framework reveals that CMIP6 models broadly capture the observed 20–30-day lag between insolation and temperature, indicating realistic land thermal inertia. However, CMIP6 models consistently overestimate temperature sensitivities to insolation, with particularly strong biases over mid-latitude and high-latitude regions in summer and winter, respectively. Applying the annual-scale polynomial transfer framework to the middle Holocene (~6000 years ago) shows that models with the highest sensitivity simulate significantly larger seasonal temperature anomalies than the lowest-sensitivity models, underscoring the impact of modern biases on orbital-scale paleoclimate simulations. The results highlight systematic overestimation of temperature–insolation sensitivity in CMIP6 models, emphasizing the importance of constraining seasonal sensitivity for robust orbital-scale climate modeling. Full article

(This article belongs to the Section Climatology)

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41 pages, 7490 KB

Open AccessArticle

Harnessing TabTransformer Model and Particle Swarm Optimization Algorithm for Remote Sensing-Based Heatwave Susceptibility Mapping in Central Asia

by Antao Wang, Linan Sun and Huicong Jia

Atmosphere 2025, 16(10), 1166; https://doi.org/10.3390/atmos16101166 - 7 Oct 2025

Abstract

This study pioneers a fully remote sensing-based framework for mapping heatwave susceptibility, integrating the TabTransformer deep learning model with Particle Swarm Optimization (PSO) for robust hyperparameter tuning. The central question addressed is whether a fully remote sensing-driven, PSO-optimized TabTransformer can achieve accurate, scalable, [...] Read more.

This study pioneers a fully remote sensing-based framework for mapping heatwave susceptibility, integrating the TabTransformer deep learning model with Particle Swarm Optimization (PSO) for robust hyperparameter tuning. The central question addressed is whether a fully remote sensing-driven, PSO-optimized TabTransformer can achieve accurate, scalable, and spatially detailed heatwave susceptibility mapping in data-scarce regions such as Central Asia. Utilizing ERA5-derived heatwave evidence and thirteen environmental and socio-economic predictors, the workflow produces high-resolution susceptibility maps spanning five Central Asian countries. Comparative analysis evidences that the PSO-optimized TabTransformer model outperforms the baseline across multiple metrics. On the test set, the optimized model achieved an RMSE of 0.123, MAE of 0.034, and R² of 0.938, outperforming the standalone TabTransformer (RMSE = 0.132, MAE = 0.038, R² = 0.93). Discriminative capacity also improved, with AUROC increasing from 0.933 to 0.940. The PSO-tuned model delivered faster convergence, lower final loss, and more stable accuracy during training and validation. Spatial outputs reveal heightened susceptibility in southern and southwestern sectors—Turkmenistan, Uzbekistan, southern Kazakhstan, and adjacent lowlands—with statistically significant improvements in spatial precision and class delineation confirmed by Chi-squared, Friedman, and Wilcoxon tests, all with congruent p-values of <0.0001. Feature importance analysis consistently identifies maximum temperature, frequency of hot days, and rainfall as dominant predictors. These advancements validate the potential of data-driven, deep learning approaches for reliable, scalable environmental hazard assessment, crucial for climate adaptation planning in vulnerable regions. Full article

(This article belongs to the Special Issue Environmental Footprints of Drought: Focusing on Emerging Issues and Their Underlying Mechanisms (2nd Edition))

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18 pages, 3187 KB

Open AccessArticle

Formaldehyde Exposure and Associated Health Burdens Apportioned to Residential and Public Places Based on Personal and Environmental Measurements

by Donghui Mo, Huimin Zhang, Yuan Wang, Fei Tuo, Mengyao Chen, Zhen Cao, Yirui Xu, Lvyan Lin, Xiaojun Liang, Daniel Mmereki, Ting Li and Zhongming Bu

Atmosphere 2025, 16(10), 1165; https://doi.org/10.3390/atmos16101165 - 7 Oct 2025

Abstract

Formaldehyde poses a critical indoor environmental health hazard, particularly in rapidly urbanizing settings. Residential and public buildings serve as the most significant exposure sites; however, the extent of urban populations’ formaldehyde exposure in these two types of environments remains unclear, posing challenges for [...] Read more.

Formaldehyde poses a critical indoor environmental health hazard, particularly in rapidly urbanizing settings. Residential and public buildings serve as the most significant exposure sites; however, the extent of urban populations’ formaldehyde exposure in these two types of environments remains unclear, posing challenges for precise prevention and control strategies. This study employed a comprehensive exposure assessment by combining personal exposure monitoring with environmental sampling to characterize formaldehyde exposure profiles and contributions apportioned to residential and public microenvironments. The mean personal exposure concentration of formaldehyde of working adults was 36.0 μg/m³ (SD: 30.7 μg/m³). The mean chronic daily intake derived from personal data was 5.1 μg/kg/day. Residential environments were identified as the predominant contributors to overall exposure (>50% of total exposure in working adults, and >80% in children/elderly), followed by public places (contributing to 40% among employed adults). For children under 5 years and the elderly, residential settings accounted for >80% of the contribution of total intake. The home and school environments contributed to approximately 60% and 30% of exposure for children and adolescents aged 5–18 years, respectively. Other microenvironments (such as vehicular and outdoor settings) contributed to less than 10%. Simulation scenarios further suggested that reducing indoor formaldehyde concentrations by 15–30% in both residential and public buildings could avert 10–20% of associated health burdens for targeted populations. These findings underscore the continuous need for formaldehyde exposure control in both residential and public environments as well as indoor health interventions in modern urban areas. Full article

(This article belongs to the Special Issue Identification and Parameter Estimation of Multi-Scale Environmental Pollution Sources)

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29 pages, 10679 KB

Open AccessArticle

Neuropsychological Effects of Air Pollution on Children and Adolescents (0–18 Years): A Global Bibliometric Analysis

by Qiurong Lei, Xingzhou Li, Xuxu Guo, Jing Chen and Chuanjian Yi

Atmosphere 2025, 16(10), 1164; https://doi.org/10.3390/atmos16101164 - 7 Oct 2025

Abstract

In recent years, increasing attention has been paid to the impact of air pollution on the neuropsychological development of children and adolescents. However, a comprehensive overview of global research trends and thematic structures in this field remains lacking. This study applies bibliometric methods [...] Read more.

In recent years, increasing attention has been paid to the impact of air pollution on the neuropsychological development of children and adolescents. However, a comprehensive overview of global research trends and thematic structures in this field remains lacking. This study applies bibliometric methods to systematically analyze 1441 English-language publications from 2000 to 2024, retrieved from the Web of Science Core Collection and Scopus. Using CiteSpace 6.4.R1, VOSviewer 1.6.20, and RStudio Bibliometrix (RStudio version: 2025.05.1+496, R version: 4.5.0, Bibliometrix package version: 5.0.0), we conducted a multidimensional visualization of publication trends, contributing countries and institutions, interdisciplinary integration, author collaborations, and keyword clustering. Results show a marked increase in research output in recent years, with the United States, China, and Spain leading in publication number and international collaboration. Key research themes include particulate pollution, prenatal and early-life exposure, and neuropsychological disorders such as attention-deficit hyperactivity and autism, alongside mechanisms like oxidative stress and neuroinflammation. This study builds a knowledge framework for the field, offering insights for scholars and evidence-based guidance for policymakers to support interventions that protect the neuropsychological health of the younger population. Full article

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

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15 pages, 517 KB

Open AccessArticle

Knowledge on Indoor Air Quality (K-IAQ): Development and Evaluation of a Questionnaire Through the Application of Item Response Theory

by Letizia Appolloni, Diego Valeri and Daniela D’Alessandro

Atmosphere 2025, 16(10), 1163; https://doi.org/10.3390/atmos16101163 - 6 Oct 2025

Abstract

Indoor air pollution is a major cause of noncommunicable diseases, and increasing people’s knowledge about the related risks is a key action for prevention. Many studies describe questionnaires for evaluating knowledge on indoor air quality that often involve selected population groups and take [...] Read more.

Indoor air pollution is a major cause of noncommunicable diseases, and increasing people’s knowledge about the related risks is a key action for prevention. Many studies describe questionnaires for evaluating knowledge on indoor air quality that often involve selected population groups and take time to fill out. This study describes the validation of a questionnaire built “ad hoc” that aims to be easy to fill out, reliable, and valid. The validation process integrated two psychometric approaches: the Classical Test Theory (CTT), which uses the Kuder–Richardson 20 (KR-20) formula to measure the internal consistency and reliability of the questionnaire as a whole, and the Item Response Theory (IRT), which evaluates each statement (item)’s validity. The questionnaire, distributed using social media to a self-selected sample of people, reached a sample of 621 subjects. In terms of internal consistency, the questionnaire was found to be satisfactory, with a KR-20 value of 0.74 (CI 0.71–0.77). The IRT analysis showed that the statements included in the questionnaire can distinguish between high-performing and low-performing interviewees, since 100% of the items reached a value of the “discrimination parameter a_j” that was within or above the recommended range. In terms of difficulty, many statements (53.3%) showed a low level of difficulty, obtaining a low “difficulty parameter b_j” value, while another 20% of the items showed a high level of difficulty. Regarding the pseudo-guessing parameter, known as the c-parameter, the probability of answering correctly for a low-performing interviewee was observed in three items (1, 6, and 9), and the same statements fell outside the range for all three parameters evaluated in the IRT. The application of the IRT highlights the criticality of some questions that would not have emerged using the CTT approach alone. Although the questionnaire is acceptable overall, it will be appropriate to evaluate whether to revise or exclude the critical questions in order to improve the instrument’s performance. Full article

(This article belongs to the Section Air Quality)

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24 pages, 5085 KB

Open AccessArticle

Investigating BTEX Emissions in Greece: Spatiotemporal Distribution, Health Risk Assessment and Ozone Formation Potential

by Panagiotis Georgios Kanellopoulos, Eirini Chrysochou and Evangelos Bakeas

Atmosphere 2025, 16(10), 1162; https://doi.org/10.3390/atmos16101162 - 4 Oct 2025

Abstract

This study investigates the atmospheric concentrations, spatiotemporal distribution, the associated health risks and the ozone formation potential of benzene, toluene, ethylbenzene and xylenes (BTEX) across 33 monitoring sites of Greece over a one-year period. Samples were collected using passive diffusive samplers and analyzed [...] Read more.

This study investigates the atmospheric concentrations, spatiotemporal distribution, the associated health risks and the ozone formation potential of benzene, toluene, ethylbenzene and xylenes (BTEX) across 33 monitoring sites of Greece over a one-year period. Samples were collected using passive diffusive samplers and analyzed by gas chromatography–mass spectrometry (GC-MS). The highest BTEX concentrations were detected during winter and autumn, particularly in urban and industrial areas such as in the Attica and Thessaloniki regions, likely due to enhanced emissions from combustion-related activities and reduced atmospheric dispersion. Health risk assessment revealed that hazard quotient (HQ) values for all compounds were within the acceptable limits. However, lifetime cancer risk (LTCR) for benzene exceeded the recommended limits in multiple regions during the colder seasons, indicating notable public health concern. Source apportionment using diagnostic ratios suggested varying seasonal emission sources, with vehicular emissions prevailing in winter and marine or industrial emissions in summer. Xylenes and toluene exhibited the highest ozone formation potential (OFP), underscoring their role in secondary pollutant formation. These findings demonstrate the need for seasonally adaptive air quality strategies, especially in Mediterranean urban and semi-urban environments. Full article

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

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25 pages, 18016 KB

Open AccessArticle

Joint Modeling of Pixel-Wise Visibility and Fog Structure for Real-World Scene Understanding

by Jiayu Wu, Jiaheng Li, Jianqiang Wang, Xuezhe Xu, Sidan Du and Yang Li

Atmosphere 2025, 16(10), 1161; https://doi.org/10.3390/atmos16101161 - 4 Oct 2025

Abstract

Reduced visibility caused by foggy weather has a significant impact on transportation systems and driving safety, leading to increased accident risks and decreased operational efficiency. Traditional methods rely on expensive physical instruments, limiting their scalability. To address this challenge in a cost-effective manner, [...] Read more.

Reduced visibility caused by foggy weather has a significant impact on transportation systems and driving safety, leading to increased accident risks and decreased operational efficiency. Traditional methods rely on expensive physical instruments, limiting their scalability. To address this challenge in a cost-effective manner, we propose a two-stage network for visibility estimation from stereo image inputs. The first stage computes scene depth via stereo matching, while the second stage fuses depth and texture information to estimate metric-scale visibility. Our method produces pixel-wise visibility maps through a physically constrained, progressive supervision strategy, providing rich spatial visibility distributions beyond a single global value. Moreover, it enables the detection of patchy fog, allowing a more comprehensive understanding of complex atmospheric conditions. To facilitate training and evaluation, we propose an automatic fog-aware data generation pipeline that incorporates both synthetically rendered foggy images and real-world captures. Furthermore, we construct a large-scale dataset encompassing diverse scenarios. Extensive experiments demonstrate that our method achieves state-of-the-art performance in both visibility estimation and patchy fog detection. Full article

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

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18 pages, 2418 KB

Open AccessArticle

Olive and Grass Pollen Concentrations: Evaluation of Forecast Models with Real Observations as Standard in the Évora Region, Portugal

by Ana Galveias, Hélder Fraga, Ana Rodrigues Costa and Célia M. Antunes

Atmosphere 2025, 16(10), 1160; https://doi.org/10.3390/atmos16101160 - 4 Oct 2025

Abstract

Background: The CAMS Regional System provides crucial, reliable pollen forecasts for allergenic pollen types. These robust predictions support the scientific and medical communities, aiding in the diagnosis, evaluation, and protection of allergic populations. So, the main goal of this study was to evaluate [...] Read more.

Background: The CAMS Regional System provides crucial, reliable pollen forecasts for allergenic pollen types. These robust predictions support the scientific and medical communities, aiding in the diagnosis, evaluation, and protection of allergic populations. So, the main goal of this study was to evaluate which model, or models best represent and simulate the olive and grass pollen data of the Évora region in the years 2021 to 2024. Results: The results showed that there are statistically significant differences between the data of the models and between the years for each of the pollen types considered. These differences were not just in pollen concentrations; they also appeared in characteristics of the pollen season, like its duration, maximum peak concentration, start date and exposure level. According to Taylor diagrams, applying moving average for normalized data, it was shown that MOCAGE best represents and simulates olive concentration data. For grass pollen SILAM, EURAD-IM and MOCAGE were the best performers. Conclusions: CAMS data can enhance the quality of life of the allergic population, as well as support the scientific and medical community to improve, assist and create mitigation measures that reduce exposure and consequently significantly reduce the occurrence of allergic disease. Full article

(This article belongs to the Special Issue Atmospheric Bioaerosols: Detection, Characterization and Modelling (2nd Edition))

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17 pages, 1935 KB

Open AccessArticle

Analysis of Stratospheric Ozone and Nitrogen Dioxide over Mid-Brazil for a Period from 2005 to 2020

by Elvira Kovač-Andrić, Vlatka Gvozdić, Brunislav Matasović, Nikola Sakač and Amaury de Souza

Atmosphere 2025, 16(10), 1159; https://doi.org/10.3390/atmos16101159 - 3 Oct 2025

Abstract

This study analyses the stratospheric concentrations of ozone (O₃) and nitrogen dioxide (NO₂) over a 16-year period (2005 to 2020) over central Brazil using satellite data with the aim of determining the influence of NO₂ on ozone distribution [...] Read more.

This study analyses the stratospheric concentrations of ozone (O₃) and nitrogen dioxide (NO₂) over a 16-year period (2005 to 2020) over central Brazil using satellite data with the aim of determining the influence of NO₂ on ozone distribution and the impact of fires and volcanic eruptions on these gases. The analysis shows that ozone and NO₂ follow seasonal patterns, with the highest concentrations occurring in September and October and the lowest from January to June. A positive correlation was found between the concentrations of ozone and NO₂, and the results of the Fourier analysis indicate semi-annual and annual cycles in the concentrations of these gases. Although there was an increase in the number of fires in the last 11 years of the study, this increase did not lead to significant changes in ozone or NO₂ concentrations, indicating the stability of these parameters in the observed area. It is presumed that the reason for the lack of changes is lower intensity of fires despite their increased number. Regarding wind patterns, it is observed that they do not differ much either which is in accordance with the fact that the monitored area is fairly close to the equator. Full article

(This article belongs to the Section Upper Atmosphere)

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15 pages, 1348 KB

Open AccessArticle

Carbon Emission Accounting and Emission Reduction Path of Container Terminal Under Low-Carbon Perspective

by Bingbing Li, Long Cheng, Huangqin Wang, Jiaren Li, Zhenyi Xu and Chengrong Pan

Atmosphere 2025, 16(10), 1158; https://doi.org/10.3390/atmos16101158 - 3 Oct 2025

Abstract

Accurate carbon emission estimation across all operational stages of container terminals is essential for advancing low-carbon development in the transportation sector and designing effective emission reduction pathways. This study develops a two-layer carbon accounting framework that integrates vessel berthing–waiting and terminal operations, tailored [...] Read more.

Accurate carbon emission estimation across all operational stages of container terminals is essential for advancing low-carbon development in the transportation sector and designing effective emission reduction pathways. This study develops a two-layer carbon accounting framework that integrates vessel berthing–waiting and terminal operations, tailored to the operational characteristics of Shanghai Port container terminals. The Ship Traffic Emission Assessment Model (STEAM) is applied to estimate emissions during berthing, while a bottom-up method is employed for mobile-mode container handling operations. Targeted mitigation strategies—such as shore power adoption, operational optimization, and “oil-to-electricity” or “oil-to-gas” transitions—are evaluated through comparative analysis. Results show that vessels generate substantial emissions during erthing, which can be significantly reduced (by over 60%) through shore power usage. In terminal operations, internal transport trucks have the highest emissions, followed by straddle carriers, container tractors, and forklifts; in stacking, tire cranes dominate emissions. Comprehensive comparisons indicate that “oil-to-electricity” can reduce total emissions by approximately 39%, while “oil-to-gas” can achieve reductions of about 73%. These findings provide technical and policy insights for supporting the green transformation of container terminals under the national dual-carbon strategy. Full article

(This article belongs to the Special Issue Anthropogenic Pollutants in Environmental Geochemistry (2nd Edition))

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