Atmosphere

20 pages, 3079 KiB

Open AccessArticle

Flow Field Modeling Analysis on Kitchen Environment with Air Conditioning Range Hood

by Xiaoying Huang, Zhihang Shen, Shunyu Zhang, Yongqiang Tan, Ang Li, Bingsong Yu, Yi Jiang, Liang Peng and Zhenlei Chen

Atmosphere 2025, 16(2), 236; https://doi.org/10.3390/atmos16020236 - 19 Feb 2025

Abstract

This study proposes a flow field modeling analysis of kitchen environments with air-conditioning range hoods. The substructure approach is applied to resolve the challenges of low computational efficiency and convergence difficulties associated with the simultaneous consideration of the range hood and the cooling [...] Read more.

This study proposes a flow field modeling analysis of kitchen environments with air-conditioning range hoods. The substructure approach is applied to resolve the challenges of low computational efficiency and convergence difficulties associated with the simultaneous consideration of the range hood and the cooling air-conditioning fan impeller rotation models. The presented approach effectively enhances computational efficiency while ensuring accuracy. A flow field analysis of the air-conditioning substructure was performed in Fluent to obtain the velocity contour plot at the air-conditioning outlet monitoring surface. The data were then mapped to the full kitchen hood model to enable a comprehensive flow field analysis of the kitchen setup. The results show that the proposed substructure-based method to analyze the flow field in kitchens with air-conditioning hoods is computationally efficient, achieving an alignment accuracy above 95% across four measurement points. These findings establish a strong foundation for future comfort assessments and the optimization of kitchens with air-conditioning hoods. Full article

(This article belongs to the Section Air Pollution Control)

22 pages, 10087 KiB

Open AccessArticle

Study on the Distribution of Gravity Wave (GW) Activity in Six Bay of Bengal Tropical Cyclones

by Kousik Nanda, Sudipta Sasmal, Raka Hazra, Abhirup Datta, Pradipta Panchadhyayee and Stelios M. Potirakis

Atmosphere 2025, 16(2), 235; https://doi.org/10.3390/atmos16020235 - 18 Feb 2025

Viewed by 115

Abstract

This study explores how the variation of Gravity Waves (GWs) is modified and intensified during tropical cyclones using high-resolution ERA5 reanalysis data. GWs play a vital role in understanding tropical cyclone dynamics due to their connection with energy and momentum transfer in the [...] Read more.

This study explores how the variation of Gravity Waves (GWs) is modified and intensified during tropical cyclones using high-resolution ERA5 reanalysis data. GWs play a vital role in understanding tropical cyclone dynamics due to their connection with energy and momentum transfer in the atmosphere. Different issues related to six tropical cyclones in the Bay of Bengal from 2019 to 2022, spanning different intensities and seasonal conditions, are analyzed. Using temperature and pressure data across 37 vertical levels, several variables like perturbation temperature and potential energy

E_{p}

profiles associated with GWs are computed. Spatial temperature distributions and

E_{p}

exhibit spiral formations resembling cyclone structures with significant altitude-dependent variations. Temperature signatures are observed at altitudes between 1.4 km and 5.8 km, with Pressure Levels (PLs) of 850 hPa and 500 hPa, respectively, varying by season and intensity, while

E_{p}

signatures are prominent between 15.25 km and and 20.77 km, with PLs of 125 hPa and PL 50 hPa, respectively, peaking at 16.58 km and PL 100 hPa for most cyclones, except Cyclone Fani, which peaked at 18.72 km with a PL of 70 hPa.

E_{p}

values range from 10 to 25 J/kg, reflecting strong GW–cyclone interactions. These findings highlight the influence of cyclone intensity, seasonality, and atmospheric dynamics on GW behavior, enhancing the understanding of energy transfer processes in the upper troposphere and lower stratosphere. Full article

(This article belongs to the Section Upper Atmosphere)

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13 pages, 2427 KiB

Open AccessArticle

The Hypothesis of the Interplay Between Air Particulate Matter PM2.5 and Acute Cellular Rejection Episodes Following Heart Transplantation

by Tomasz Urbanowicz, Krzysztof Skotak, Dominika Konecka-Mrówka, Hanna Wachowiak-Baszyńska, Rafał Skowronek, Jędrzej Sikora, Jakub Bratkowski, Jan Kaczmarek, Maksymilian Misiorny, Ewa Straburzyńska-Migaj, Jerzy Nożyński and Marek Jemielity

Atmosphere 2025, 16(2), 234; https://doi.org/10.3390/atmos16020234 - 18 Feb 2025

Viewed by 174

Abstract

Background: In end-stage HF, interventional therapy is the treatment of choice, including mechanical circulatory support and heart organ transplantation. Acute cellular rejection is considered a major impediment to the long-term survival of cardiac allografts. The aim of this study is to point out a [...] Read more.

Background: In end-stage HF, interventional therapy is the treatment of choice, including mechanical circulatory support and heart organ transplantation. Acute cellular rejection is considered a major impediment to the long-term survival of cardiac allografts. The aim of this study is to point out a possible relationship underlying acute cellular rejection risk in heart organ recipients. Methods: A total of 30 (25 (83%) men and 5 (17%) women) heart organ recipients with a median (Q1–Q3) age of 49 (38–60) were enrolled in the analysis. The results from repeated hospitalizations due to protocolar endomyocardial biopsies performed between one and three months following the heart transplantation in relation air pollution exposure were taken into the analysis. Results: The median (Q1–Q3) observation time after organ transplantation was 92 (82–97) days. A significant difference in PM2.5 exposure between the rejection group (16.10 (14.24–17.61)) μg/m³ and the non-rejection group (11.97 (9.85–12.97)) μg/m³ was noticed (p < 0.001). The odds ratio (95% confidence interval) for acute rejection prediction related to PM2.5 was 1.79 (1.11–2.89), p = 0.018. The reviewer operator curve for acute cellular rejection related to PM2.5 exposure was performed, and the area under the curve (AUC) was 0.873, yielding a precision of 0.600 and an f-measure of 0.545. The predicted residual plots for PM2.5 indicated a 50% increased risk for PM2.5 above 16 μg/m³ and of 91% for PM2.5 above 20 μg/m³. Conclusions: The single-center study was performed on a limited number of heart organ recipients and was related to personalized individual calculations of PM2.5 exposure. The study represents a personalized approach and indicates possible links to the hypothesis, which should be verified on a higher volume of patients. Full article

(This article belongs to the Special Issue Cutting-Edge Developments in Air Quality and Health)

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24 pages, 6645 KiB

Open AccessArticle

Assessing the Impacts of Transition and Physical Climate Risks on Industrial Metal Markets: Evidence from the Novel Multivariate Quantile-on-Quantile Regression

by Ousama Ben-Salha, Mourad Zmami, Sami Sobhi Waked, Bechir Raggad, Faouzi Najjar and Yazeed Mohammad Alenazi

Atmosphere 2025, 16(2), 233; https://doi.org/10.3390/atmos16020233 - 18 Feb 2025

Viewed by 130

Abstract

Climate change and global warming have been shown to increase the frequency and intensity of extreme weather events. Concurrently, substantial efforts are being directed toward fostering the transition to a low-carbon economy. These concurrent trends result in the emergence of both physical and [...] Read more.

Climate change and global warming have been shown to increase the frequency and intensity of extreme weather events. Concurrently, substantial efforts are being directed toward fostering the transition to a low-carbon economy. These concurrent trends result in the emergence of both physical and transition climate risks. This study investigates the impacts of climate risks, both physical and transition, on the return of major industrial metals (aluminum, copper, iron, lead, tin, nickel, and zinc) between January 2005 and December 2023. Employing the novel multivariate quantile-on-quantile regression (m-QQR) approach, this study examines how climate risks affect metal markets under different market conditions and risk levels. The results reveal that transition risks exert a more significant adverse impact on metal returns during bearish markets conditions, particularly for metals linked to high-emission industries, while physical risks affect metal returns across a wider range of quantiles, often increasing volatility during extreme market conditions. Furthermore, copper and nickel, both of which are crucial for renewable energy development, demonstrate resilience at higher quantiles, highlighting their role in the transition to a low-carbon economy. Finally, these two metals may serve as effective hedges against losses in other metals that are more vulnerable to transition risks, like aluminum and lead. Full article

(This article belongs to the Special Issue Climate Change and Extreme Weather Disaster Risks)

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21 pages, 27128 KiB

Open AccessArticle

Spatiotemporal Dynamics of PM_2.5-Related Premature Deaths and the Role of Greening Improvement in Sustainable Urban Health Governance

by Peng Tang, Tianshu Liu, Xiandi Zheng and Jie Zheng

Atmosphere 2025, 16(2), 232; https://doi.org/10.3390/atmos16020232 - 18 Feb 2025

Viewed by 82

Abstract

Environmental particulate pollution is a major global environmental health risk factor, which is associated with numerous adverse health outcomes, negatively impacting public health in many countries, including China. Despite the implementation of strict air quality management policies in China and a significant reduction [...] Read more.

Environmental particulate pollution is a major global environmental health risk factor, which is associated with numerous adverse health outcomes, negatively impacting public health in many countries, including China. Despite the implementation of strict air quality management policies in China and a significant reduction in PM_2.5 concentrations in recent years, the health burden caused by PM_2.5 pollution has not decreased as expected. Therefore, a comprehensive analysis of the health burden caused by PM_2.5 is necessary for more effective air quality management. This study makes an innovative contribution by integrating the Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI), and Soil-Adjusted Vegetation Index (SAVI), providing a comprehensive framework to assess the health impacts of green space coverage, promoting healthy urban environments and sustainable development. Using Nanjing, China, as a case study, we constructed a health impact assessment system based on PM_2.5 concentrations and quantitatively analyzed the spatiotemporal evolution of premature deaths caused by PM_2.5 from 2000 to 2020. Using Multiscale Geographically Weighted Regression (MGWR), we explored the impact of greening improvement on premature deaths attributed to PM_2.5 and proposed relevant sustainable governance strategies. The results showed that (1) premature deaths caused by PM_2.5 in Nanjing could be divided into two stages: 2000–2015 and 2015–2020. During the second stage, deaths due to respiratory and cardiovascular diseases decreased by 3105 and 1714, respectively. (2) The spatial variation process was slow, with the overall evolution direction predominantly from the southeast to northwest, and the spatial distribution center gradually shifted southward. On a global scale, the Moran’s I index increased from 0.247251 and 0.240792 in 2000 to 0.472201 and 0.468193 in 2020. The hotspot analysis revealed that high–high correlations slowly gathered toward central Nanjing, while the proportion of cold spots increased. (3) The MGWR results indicated a significant negative correlation between changes in green spaces and PM_2.5-related premature deaths, especially in densely vegetated areas. This study comprehensively considered the spatiotemporal changes in PM_2.5-related premature deaths and examined the health benefits of green space improvement, providing valuable references for promoting healthy and sustainable urban environmental governance and air quality management. Full article

(This article belongs to the Section Air Quality)

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Study Area. (A) the map of China. (B) the administrative boundary of Jiangsu Province. (C) the administrative boundary and population distribution of Nanjing City. Full article ">Figure 2
Spatial distribution of premature deaths from pM2.5-related diseases (2000–2020). (A) is the spatial distribution of premature deaths caused by respiratory diseases due to PM2.5; (B) is the spatial distribution of premature deaths caused by cardiovascular diseases due to PM2.5. Full article ">Figure 3
Changes in number of premature deaths from PM2.5-related diseases. Full article ">Figure 4
Standard deviation ellipses (A1): respiratory diseases; (B1): cardiovascular diseases. (A`,B`) the change of the center of the standard deviation ellipse. Full article ">Figure 5
Cold and hot spot analysis (A–E): spatial distributions of cold and hot spots for PM2.5-induced premature deaths from respiratory diseases; (F–J): spatial distributions of cold and hot spots for PM2.5-induced premature deaths from cardiovascular diseases.The numbers in the image represent Z-scores. Positive values indicate high-value clusters, while negative values indicate low-value clusters. Full article ">Figure 6
Change in proportions of cold and hot spot areas. R means the area proportion of respiratory system diseases; C means the area proportion of cardiovascular diseases. Full article ">Figure 7
Multiscale Geographically Weighted Regression analysis of vegetation indices and HIA results. (A–C) the regression results of respiratory diseases caused by PM2.5 and vegetation index. (D–F) the regression results of cardiovascular diseases caused by PM2.5 and vegetation index. Full article ">

17 pages, 2320 KiB

Open AccessArticle

An AI-Based Framework for Characterizing the Atmospheric Fate of Air Pollutants Within Diverse Environmental Settings

by Nataša Radić, Mirjana Perišić, Gordana Jovanović, Timea Bezdan, Svetlana Stanišić, Nenad Stanić and Andreja Stojić

Atmosphere 2025, 16(2), 231; https://doi.org/10.3390/atmos16020231 - 18 Feb 2025

Viewed by 93

Abstract

This study introduces a novel artificial intelligence (AI) modeling framework that combines machine learning algorithms optimized through metaheuristics with explainable AI to capture complex interactions among pollutant concentrations, meteorological data, and socio-economic indicators. Applied to a COVID-19-related dataset comprising 404 variables, with benzene [...] Read more.

This study introduces a novel artificial intelligence (AI) modeling framework that combines machine learning algorithms optimized through metaheuristics with explainable AI to capture complex interactions among pollutant concentrations, meteorological data, and socio-economic indicators. Applied to a COVID-19-related dataset comprising 404 variables, with benzene concentrations as the target—measured using proton transfer reaction–mass spectrometry in Belgrade, Serbia—the framework demonstrated exceptional sensitivity in assessing the impact of complex environmental and societal changes during the pandemic. Explainable AI techniques, such as SHAP and SAGE, were employed to reveal the influence of each predictor, while the clustering of SHAP values identified distinct environmental settings that influenced benzene behavior. Three distinct settings were identified regarding benzene levels during the onset of the state of emergency. The first, involving local petroleum-related activities, biomass burning, chemical manufacturing, and traffic, led to a 15.7% reduction in benzene levels. The second, characterized by non-combustion processes, nocturnal chemistry, and the specific meteorological context, resulted in a 51.9% increase. The third, driven by local industrial processes, contributed to a modest 2.33% reduction. The study underscored the critical role of environmental settings in shaping air pollutant behavior, emphasizing the importance of integrating broader environmental contexts into models to gain a more comprehensive understanding of air pollutants and their dynamics. Full article

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

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13 pages, 2433 KiB

Open AccessArticle

Potential Health Risk of Dust from Stone Mill Industries

by Kanokporn Swangjang, Arnol Dantrakul and Kamolchanok Panishkan

Atmosphere 2025, 16(2), 230; https://doi.org/10.3390/atmos16020230 - 18 Feb 2025

Viewed by 132

Abstract

Stone mill operations contribute significantly to air pollution and increase health risks not only for workers but also for nearby communities. This study aimed to assess the health impacts of stone mill industries on nearby residents. The research was conducted in two areas: [...] Read more.

Stone mill operations contribute significantly to air pollution and increase health risks not only for workers but also for nearby communities. This study aimed to assess the health impacts of stone mill industries on nearby residents. The research was conducted in two areas: a primary region with a high number of stone mills and an area without stone mills. A questionnaire-based survey was employed, and potential health risks were evaluated using the hazard quotient (HQ) method. Total suspended particulates (TSPs) and particulate matter-10 micron (PM10) were analyzed as hazard factors based on monitoring data from seven stone mills collected between 2008 and 2021. The study found that residents in major stone mill areas reported higher hazard quotients (HQs) than those living farther from the mills, with a statistically significant association (p < 0.01). Seasonal variations also influenced dust distribution, with the highest TSP and PM10 levels recorded during winter, exacerbating health risks for local populations. This study highlights the need for improved community settlement planning, consideration of meteorological conditions, regulatory interventions by relevant agencies, and enhancements in environmental monitoring systems to mitigate the adverse health effects of stone mill operations. Full article

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

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23 pages, 26773 KiB

Open AccessArticle

Suitability of CMIP6 Models Considering Statistical Downscaling Based on GloH2O and E-OBS Dataset in River Basin Districts of the Southeastern Baltic Sea Basin

by Vytautas Akstinas, Karolina Gurjazkaitė, Diana Meilutytė-Lukauskienė and Darius Jakimavičius

Atmosphere 2025, 16(2), 229; https://doi.org/10.3390/atmos16020229 - 18 Feb 2025

Viewed by 133

Abstract

Climate projections based on global climate models (GCMs) are generally subject to large uncertainties, as the models only reflect the local climate in the past to a limited extent. Statistical downscaling is the most cost-effective approach to identify the systematic biases of the [...] Read more.

Climate projections based on global climate models (GCMs) are generally subject to large uncertainties, as the models only reflect the local climate in the past to a limited extent. Statistical downscaling is the most cost-effective approach to identify the systematic biases of the GCMs from the past and eliminate them in the projections. This study seeks to evaluate the effectiveness of GCMs in capturing local climatic characteristics at the river basin district scale by applying gridded statistical downscaling techniques using global and regional datasets. The historical observational datasets E-OBS and GloH2O were selected to downscale the raw data of 17 GCMs from ~1° grid cells to 0.25° resolution. E-OBS is a regional dataset supported by a dense network of meteorological stations in Europe, while GloH2O is a global dataset covering all continents. The results show that the suitability of the GCMs varies depending on the selected parameter. The statistical downscaling revealed the advantages of the performance of E-OBS in representing local climate characteristics during the historical period and emphasized the crucial role of regional datasets for good climate depiction. Such an approach provides the possibility to assess the relative performance of GCMs based on the high-resolution observational and reanalysis datasets, while generating statistically downscaled datasets for the best ranked GCMs. The strategies used in this study can help to identify the most appropriate models to assemble the right ensemble of GCMs for specific studies. Full article

(This article belongs to the Special Issue The Hydrologic Cycle in a Changing Climate)

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15 pages, 7070 KiB

Open AccessArticle

Assessment of Fire Dynamics in the Amazon Basin Through Satellite Data

by Humberto Alves Barbosa, Catarina Oliveira Buriti and Tumuluru Venkata Lakshmi Kumar

Atmosphere 2025, 16(2), 228; https://doi.org/10.3390/atmos16020228 - 18 Feb 2025

Viewed by 86

Abstract

The Amazon region is becoming more vulnerable to wildfires occurring in the dry season, a crisis amplified by climate change, which affects biomass burning across a wide range of forest environments. In this study, we examined the impact of seasonal fire on greenhouse [...] Read more.

The Amazon region is becoming more vulnerable to wildfires occurring in the dry season, a crisis amplified by climate change, which affects biomass burning across a wide range of forest environments. In this study, we examined the impact of seasonal fire on greenhouse (GHG) emissions over the study region during the last two decades of the 21st century by integrating calibrated and validated satellite-derived products of estimations of burned biomass area, land cover, vegetation greenness, rainfall, land surface temperature (LST), carbon monoxide (CO), and nitrogen dioxide (NO₂) through geospatial techniques. The results revealed a strong impact of fire activity on GHG emissions, with abrupt changes in CO and NO₂ emission factors between early and middle dry season fires (July–September). Among these seven variables analyzed, we found a positive relationship between the total biomass burned area and fire-derived GHG emission factors (r² = 0.30) due to the complex dynamics of plant moisture and associated CO and NO₂ emissions generated by fire. Nevertheless, other land surface drivers showed the weakest relationships (r²~0.1) with fire-derived GHG emissions due to other factors that drive their regional distribution. Our analysis suggests the importance of continued research on the response of fire season to other land surface characteristics that represent the processes driving fire over the study region such as fuel load, composition, and structure, as well as prevailing weather conditions. These determinants drive fire-related GHG emissions and fire-related carbon cycling relationships and can, therefore, appropriately inform policy fire-abatement guidelines. Full article

(This article belongs to the Section Air Quality)

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The Amazon basin. (a) the spatial distribution of annual burned biomass areas from 2001 to 2020 in four distinct seasons. The colors of each plot indicate: rainy season (December to June), early dry season (July), middle dry season (August–September), or late dry season (October–November). (b) The monthly distribution of total number of fire events identified in the years 2015 to 2020. Full article ">Figure 2
Schematic representation of the hydrographic network superimposed over the Amazon basin. (a) Average annual rainfall from the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) for the baseline (2001–2020). (b) The land-cover map. (c) Topographic relief based on 250 m Digital Elevation Model—Shuttle Radar Topographic Mission (DEM-SRTM) images. (d) Köppen–Geiger climate classification map. Full article ">Figure 3
(a) The annual forest cover change (% of grid cell area) between 2001 and 2020 calculated using the percentage change method applied to the MODIS land-cover data. (b) Variability of monthly normalized values of NDVI (the green curve), CO (the orange curve), NO2 (the yellow curve), and burned biomass (the red curve) parameters for their available periods between 2001 and 2020 over the study region. The three parameters (CO, NO2, and burned biomass) were normalized to be between 0 and 1, using the min-max normalization method. Fire-derived CO and NO2 emission parameters are usually quantified using an emission factor, a ratio indicating the proportion of each chemical species that is emitted per unit of biomass burning. Full article ">Figure 4
Scatterplot of (a) six different parameters of principal components (PC2 versus PC1 loadings). Scatterplot of (b) fire per month of PC2 versus PC1 loadings. The percentage indicates the total variance. The arrow widths on the figures are proportional to the PC loadings (positive or negative) regressed upon a fire driver and fire per month. Color symbols indicate mean loadings of PC1 and PC2. Full article ">

17 pages, 3922 KiB

Open AccessArticle

Future Climate Trends in the Wine-Growing Regions of the Western Cape of South Africa

by Helga Chauke and Rita Pongrácz

Atmosphere 2025, 16(2), 227; https://doi.org/10.3390/atmos16020227 - 18 Feb 2025

Viewed by 164

Abstract

The Western Cape province is among the best wine-producing regions; however, the province has been facing environmental challenges due to the rapidly changing climate. This study analyses the projected changes in temperature and precipitation patterns across seven wine-producing districts in the Western Cape [...] Read more.

The Western Cape province is among the best wine-producing regions; however, the province has been facing environmental challenges due to the rapidly changing climate. This study analyses the projected changes in temperature and precipitation patterns across seven wine-producing districts in the Western Cape using high-resolution regional climate model (RCM) simulations from the CORDEX-Africa framework. The model simulations highlight spatial variability due to variable topography and microclimates within the province wherein variable changes in climate conditions are projected over the selected wine-producing regions. The Olifants River is projected to experience the most substantial temperature increases—greater than 5 °C—under the very high baseline emission scenario at the end of the century, while regions closer to the coast, such as Cape Agulhas, are projected to experience moderate changes in both temperature and precipitation changes. The projected changes in precipitation indicate a strong drying trend, especially over regions that are farther from the south coast. The results highlight the vulnerability of vinicultural practices due to climate change; therefore, this study can be used to provide stakeholders with information needed so that they can adapt to the changing climate conditions across wine regions. Full article

(This article belongs to the Section Climatology)

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36 pages, 9488 KiB

Open AccessArticle

New Challenges for Tropical Cyclone Track and Intensity Forecasting in Unfavorable External Environment in Western North Pacific. Part I. Formations South of 20° N

by Russell L. Elsberry, Hsiao-Chung Tsai, Wen-Hsin Huang and Timothy P. Marchok

Atmosphere 2025, 16(2), 226; https://doi.org/10.3390/atmos16020226 - 18 Feb 2025

Viewed by 192

Abstract

A pre-operational test started in mid-July 2024 to demonstrate the capability of the ECMWF’s ensemble (ECEPS) to predict western North Pacific Tropical Cyclones (TCs) lifecycle tracks and intensities revealed new forecasting challenges for four typhoons that started well south of 20° N. As [...] Read more.

A pre-operational test started in mid-July 2024 to demonstrate the capability of the ECMWF’s ensemble (ECEPS) to predict western North Pacific Tropical Cyclones (TCs) lifecycle tracks and intensities revealed new forecasting challenges for four typhoons that started well south of 20° N. As Typhoon Gaemi (05 W) was moving poleward into an unfavorable environment north of 20° N, a sharp westward turn to cross Taiwan was a challenge to forecast. The pre-Yagi (12 W) westward turn across Luzon Island, re-formation, and then extremely rapid intensification prior to striking Hainan Island were challenges to forecast. The slow intensification of Bebinca (14 W) after moving poleward across 20° N into an unfavorable environment was better forecast by the ECEPS than by the Joint Typhoon Warning Center (JTWC), which consistently over-predicted the intensification. An early westward turn south of 20° N by Kong-Rey (23 W) leading to a long westward path along 17° N and then a poleward turn to strike Taiwan were all track forecasting challenges. Four-dimensional COAMPS-TC Dynamic Initialization analyses utilizing high-density Himawari-9 atmospheric motion vectors are proposed to better define the TC intensities, vortex structure, and unfavorable environment for diagnostic studies and as initial conditions for regional model predictions. In Part 2 study of selected 2024 season TCs that started north of 20° N, more challenging track forecasts and slow intensification rates over an unfavorable TC environment will be documented. Full article

(This article belongs to the Special Issue Typhoon/Hurricane Dynamics and Prediction (2nd Edition))

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23 pages, 4221 KiB

Open AccessReview

Effects of Cadmium Pollution on Human Health: A Narrative Review

by Yunxi Yang, Mohammad Farooque Hassan, Waseem Ali, Hui Zou, Zongping Liu and Yonggang Ma

Atmosphere 2025, 16(2), 225; https://doi.org/10.3390/atmos16020225 - 17 Feb 2025

Viewed by 139

Abstract

Cadmium (Cd) is a pervasive environmental and industrial toxin that poses significant health risks. It readily moves through soil–plant systems, leading to global contamination and human exposure through diet, smoking, and pollution. The main purpose of this review is to explore the effect [...] Read more.

Cadmium (Cd) is a pervasive environmental and industrial toxin that poses significant health risks. It readily moves through soil–plant systems, leading to global contamination and human exposure through diet, smoking, and pollution. The main purpose of this review is to explore the effect of Cd on physiological processes of different bodies’ organs, including the bones, kidneys, and liver, as well as the immune, cerebrovascular, cardiovascular, and reproductive systems. Accumulation of Cd in the body can result in poisoning with severe impacts on bone and kidney health, as well as reduced bone mineral density due to renal damage. Research has linked Cd to lung cancer and pulmonary toxicity, and elevated urinary biomarkers suggest compromised renal function. Cd also affects the cardiovascular, cerebrovascular, and immune systems; the liver; and reproductive systems, contributing to various diseases by disrupting blood pressure and calcium regulation, causing oxidative stress and DNA damage, and impairing cell functions. Ongoing research is essential to fully understand Cd-induced toxicological effects and to develop effective interventions to prevent exposure and mitigate health risks. Full article

(This article belongs to the Special Issue Development in Atmospheric Dispersion Modelling)

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The influence of Cd exposure on pulmonary function decline, autophagy activation, and apoptosis elevation in COPD patients. Full article ">Figure 2
Impact of Cd on bone health leading to osteoporosis: The figure includes images of normal compact bone and osteoporotic spongy bone, and it lists symptoms such as height loss, back pain, stooped posture, bone fractures without major trauma, and bone or joint pain. Full article ">Figure 3
Activation of Kupffer cells and macrophages due to Cd exposure in pubertal mice: Cd exposure activates Kupffer cells (M1) and macrophages (M2), releasing both anti-inflammatory and pro-inflammatory cytokines. The signaling pathways involve NLRP3, ASC, and caspase-1, which regulate pro-inflammatory cytokines. Additionally, MCP1 plays a role in the recruitment and activation of macrophages. The presence of pubertal mice in this study provides a model to investigate these processes. Full article ">Figure 4
Impact of Cd on kidney cells and mitochondrial function. This figure illustrates the pathways through which Cd affects kidney cells, focusing on mitochondrial dysfunction. Cd exposure leads to increased reactive oxygen species (ROS), disruption of the electron transport system (ETS), and insufficient antioxidant response, resulting in decreased ATP production and mitochondrial fission. Genetic interactions include PGC-1α activation by Sirt1 and FOX-3, while mitochondrial biogenesis is reduced. The figure also shows the processes of replacing damaged mtDNA, increased apoptosis due to severe mitochondrial damage, and mitophagy for removing damaged mitochondria. Full article ">Figure 5
Exposure to Cd has pro-inflammatory and pro-oxidative effects on neutrophils and macrophages. It leads to an increase in inflammatory cytokines and chemokines, resulting in the recruitment of neutrophils and macrophages as well as tissue damage. Cd also up-regulates caspase 3, leading to cell apoptosis. Additionally, it elevates the levels of reactive oxygen species (ROS), cyclooxygenase-2 (COX2), matrix metallopeptidases (MMPs), leukotriene B4 (LTB4), nuclear factor kappa-light-chain-enhancer of activated B cells (NFkB), activator protein 1 (AP-1), macrophage inflammatory protein 2-alpha (MIP-2), interleukin 6 (IL-6), interleukin 8 (IL-8), tumor necrosis factor-alpha (TNF-a), and interleukin 1 beta (IL-1b). Full article ">Figure 6
The diagram specific cellular pathways and nuclear interactions associated with Cd. It includes annotations such as active ATPase transporter (AT), passive channel or carrier (PC), G-protein-coupled estrogen receptor-30 (GPR30), G-protein (G), metallothionein (MT), glutathione (GSH), glutathione peroxidase (GPx), reactive oxygen species (ROS), and endoplasmic reticulum (ER). Full article ">

19 pages, 19605 KiB

Open AccessArticle

Skill Validation of High-Impact Rainfall Forecasts over Vietnam Using the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) and Dynamical Downscaling with the Weather Research and Forecasting Model

by Tran Anh Duc, Mai Van Khiem, Mai Khanh Hung, Dang Dinh Quan, Do Thuy Trang, Hoang Gia Nam, Lars R. Hole and Du Duc Tien

Atmosphere 2025, 16(2), 224; https://doi.org/10.3390/atmos16020224 - 16 Feb 2025

Viewed by 286

Abstract

This research evaluates the quality of high-impact rainfall forecasts across Vietnam and its sub-climate regions. The 3-day rainfall forecast products evaluated include the European Centre for Medium-Range Weather Forecasts (ECMWF) High-Resolution Integrated Forecasting System (IFS) and its downscaled outputs using the Weather Research [...] Read more.

This research evaluates the quality of high-impact rainfall forecasts across Vietnam and its sub-climate regions. The 3-day rainfall forecast products evaluated include the European Centre for Medium-Range Weather Forecasts (ECMWF) High-Resolution Integrated Forecasting System (IFS) and its downscaled outputs using the Weather Research and Forecasting (WRF) model with the Advanced Research WRF core (WRF-ARW): direct downscaling and downscaling with data assimilation. A full 5-year validation period from 2019 to 2025 was processed. The validation focused on basic rainfall thresholds and also considered the distribution of skill scores for intense events and extreme events. The validations revealed systematic errors (bias) in the models at low rainfall thresholds. The forecast skill was the lowest for northern regions, while the central regions exhibited the highest. For regions strongly affected by terrain, high-resolution downscaling with local observation data assimilation is necessary to improve the detectability of extreme events. Full article

(This article belongs to the Special Issue Precipitation Observations and Prediction (2nd Edition))

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4 pages, 134 KiB

Open AccessEditorial

Special Issue Editorial: Urban and Regional Nitrogen Cycle and Risk Management

by Chaofan Xian, Yu-Sheng Shen and Cheng Gong

Atmosphere 2025, 16(2), 223; https://doi.org/10.3390/atmos16020223 - 16 Feb 2025

Viewed by 157

Abstract

Disturbance of urban and regional nitrogen cycles due to urbanization have resulted in the greenhouse effect, acid rain, eutrophication, and reductions in biodiversity [...] Full article

(This article belongs to the Special Issue Urban and Regional Nitrogen Cycle and Risk Management)

22 pages, 9872 KiB

Open AccessArticle

Temperature and Precipitation Extremes in the Brazilian Legal Amazon: A Summary of Climatological Patterns and Detected Trends

by Wanderson Luiz-Silva, Anna Carolina Fernandes Bazzanela, Claudine Pereira Dereczynski, Antonio Carlos Oscar-Júnior and Igor Pinheiro Raupp

Atmosphere 2025, 16(2), 222; https://doi.org/10.3390/atmos16020222 - 16 Feb 2025

Viewed by 207

Abstract

The continuous understanding of extreme weather events in the Amazon is fundamental due to the importance of this biome for the regional and planetary climate system. Climate characterization and the identification of changes in the current climate can be key findings for adaptation [...] Read more.

The continuous understanding of extreme weather events in the Amazon is fundamental due to the importance of this biome for the regional and planetary climate system. Climate characterization and the identification of changes in the current climate can be key findings for adaptation and mitigation measures. This study examined climatology and trends in 20 climate extreme indices associated with air temperature and precipitation in the Brazilian Legal Amazon (BLA). Daily observed data, interpolated at grid points, were analyzed from 1961 to 2020. Statistical tests were employed to determine the trend’s significance and magnitude. The results indicate that prolonged heat, hot days, and annual temperature records have become increasingly frequent in practically all of BLA over the last decades. Warm days and nights are increasing at approximately +11 days/decade. Heat waves have gone from 10 to 20 consecutive days on average in the 1960s to around 30–40 days in recent years. Indices associated with the intensity and frequency of extreme precipitation show a reduction, especially in the rainiest portion of the BLA, the western sector. In the east/south region of BLA, where consecutive dry days reach 100 days/year, they continue to increase at a rate of +1.5 days/decade, a fact related to the delay at the beginning of the rainy season. These aspects deserve attention since they impact local circulation, reducing the convergence of humidity not only over the BLA but also in central-southern region of Brazil. Full article

(This article belongs to the Special Issue Extreme Weather Events in a Warming Climate)

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13 pages, 16029 KiB

Open AccessArticle

Numerical Simulation of Perkins Instability in the Midlatitude F-Region Ionosphere: The Influence of Background Ionospheric Multi-Factors

by Yi Liu, Ting Lan, Yufeng Zhou, Yunzhou Zhu, Zhiqiang Fan, Yewen Wu, Yuqiang Zhang and Xiang Wang

Atmosphere 2025, 16(2), 221; https://doi.org/10.3390/atmos16020221 - 16 Feb 2025

Viewed by 233

Abstract

A numerical simulation of Perkins instability in the midlatitude F-region ionosphere is developed in this study. The growth of nighttime plasma density perturbation excited by Perkins instability was successfully reproduced. The simulated results show that the ionospheric perturbation structure elongated from northwest (NW) [...] Read more.

A numerical simulation of Perkins instability in the midlatitude F-region ionosphere is developed in this study. The growth of nighttime plasma density perturbation excited by Perkins instability was successfully reproduced. The simulated results show that the ionospheric perturbation structure elongated from northwest (NW) to southeast (SE) was generated from initial random seeding by applying a very large southeastward neutral wind (200 m/s). The domain wave vector direction agreed with the linear Perkins theory. Our simulated results were consistent with the previous observations and simulations. To investigate the influence of background ionospheric multi-factors on the generation of nighttime medium-scale traveling ionospheric disturbance (MSTID), we simulated the evolution process of ionospheric perturbations under initial background ionospheric conditions. The simulated results indicate the importance of neutral scale height on the development of nighttime MSTID and suggest that a smaller neutral scale height would amplify the amplitude of ionospheric perturbations. The influences of gravity wave (GW) activity and polarized electric field seeding from plasma instability in the E region are also discussed in this study. We conclude that the additional seeding processes play a major role in the accelerated Perkins instability and amplify ionospheric perturbations. The electrodynamic coupling process has a greatly significant effect on the growth rate of Perkins instability compared to GW activity. Full article

(This article belongs to the Section Planetary Atmospheres)

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The evolution process of relative plasma density perturbation at 280 km for 0 s (a), 1200 s (b), 2400 s (c), and 3600 s (d) from random perturbation conditions with a range of 0–500 m. Full article ">Figure 2
Power spectral density of density perturbation in the wave vector domain in the height of 280 km for t = 3600 s. The area composed of solid lines indicates the region where the Perkins instability occurs. Full article ">Figure 3
Time variation of mean field-line-integrated Pedersen conductivity perturbation. Full article ">Figure 4
Neutral density scale height dependence of Perkins instability. The neutral density scale heights are (a) 60 km, (b) 80 km, (c) 100 km, and (d) 120 km, respectively. Full article ">Figure 5
The variation of the mean amplitude of relative density perturbation with a neutral density scale height at 280 km for 3600 s. Full article ">Figure 6
The evolution process of relative field-line-integrated Pedersen conductivity perturbation at 0 s (a), 1200 s (b), 2400 s (c), and 3600 s (d) under the action of GW activity. Full article ">Figure 7
The evolution process of relative field-line-integrated Pedersen conductivity perturbation at 0 s (a), 120 s (b), 600 s (c), and 1200 s (d)under the action of E region polarized electric field. Full article ">

11 pages, 932 KiB

Open AccessArticle

Combined Effects of Particulate Matter Exposure and Exercise Training on Neuroplasticity-Related Growth Factors and Blood–Brain Barrier Integrity

by Su-Youn Cho and Hee-Tae Roh

Atmosphere 2025, 16(2), 220; https://doi.org/10.3390/atmos16020220 - 15 Feb 2025

Viewed by 285

Abstract

Particulate matter (PM) negatively impacts brain health, while exercise training can mitigate these effects and promote brain health. However, the effect of the interaction between PM exposure and exercise on brain health remains insufficiently explored. This study investigated the effects of PM exposure [...] Read more.

Particulate matter (PM) negatively impacts brain health, while exercise training can mitigate these effects and promote brain health. However, the effect of the interaction between PM exposure and exercise on brain health remains insufficiently explored. This study investigated the effects of PM exposure and exercise training on neuroplasticity-related growth factors and blood–brain barrier (BBB) integrity. Forty male C57BL/6 mice were randomly assigned to four groups as follows: control (CON, n = 10), PM exposure (PM, n = 10), exercise training (EX, n = 10), and PM exposure with exercise training (PMEX, n = 10). PM exposure and exercise training interventions were conducted over 8 weeks. The PM group showed significantly elevated levels of malondialdehyde (MDA), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), S100 calcium-binding protein β (S100β), and neuron-specific enolase (NSE) (p < 0.05) and reduced levels of superoxide dismutase (SOD), insulin-like growth factor-1 (IGF-1), brain-derived neurotrophic factor (BDNF), and vascular endothelial growth factor (VEGF) (p < 0.05) compared to the CON and EX groups. Conversely, the EX group demonstrated significantly reduced MDA, IL-6, TNF-α, S100β, and NSE levels (p < 0.05) and enhanced SOD, IGF-1, BDNF, and VEGF levels (p < 0.05) compared to the PM group. However, the PMEX group exhibited attenuated benefits, showing higher MDA, IL-6, TNF-α, S100β, and NSE levels (p < 0.05) and lower SOD and IGF-1 levels (p < 0.05) compared to the EX group. These findings suggest that PM exposure induces oxidative stress, inflammation, and BBB disruption while downregulating neuroplasticity-related growth factors. Exercise training mitigates these adverse effects by enhancing antioxidant activity, reducing inflammation, upregulating neuroplasticity-related growth factors, and preserving BBB integrity. However, the protective effects of exercise may be partially diminished under conditions of PM exposure. Full article

(This article belongs to the Special Issue New Insights into Ambient Air Pollution and Human Health)

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23 pages, 1974 KiB

Open AccessArticle

Effect of Synchronization Between Millihertz Geomagnetic Field Variations and Human Heart Rate Oscillations During Strong Magnetic Storms

by Tatiana A. Zenchenko, Natalia I. Khorseva, Tamara K. Breus, Andrey V. Drozdov and Olga Y. Seraya

Atmosphere 2025, 16(2), 219; https://doi.org/10.3390/atmos16020219 - 15 Feb 2025

Viewed by 232

Abstract

Protecting people with various diseases from the adverse effects of space weather factors requires an understanding of their effects on healthy people who participate in heliobiological research as a ‘control group’. This study aimed to investigate the effect of human heart-rate synchronization with [...] Read more.

Protecting people with various diseases from the adverse effects of space weather factors requires an understanding of their effects on healthy people who participate in heliobiological research as a ‘control group’. This study aimed to investigate the effect of human heart-rate synchronization with variations in the geomagnetic field of the ULF frequency range (1–5 mHz) (“biogeosynchronization effect”). We analyzed 61 electrocardiogram recordings of 100 min that were obtained on 24–27 September 2023, 10–13 May 2024 and 10–13 October 2024 from two female volunteers in good health. The biogeosynchronization effect was observed in 69% of cases. The probability of its occurrence correlates with the Dst index (correlation coefficient Rs = 0.313, p = 0.014); there is no correlation with the amplitude of the ULF oscillations. It has been shown that biogeosynchronization is mainly manifested during the recovery phase of magnetic storms, provided that at this time, the geomagnetic ULF oscillations are in phase at large distances along the observation meridian (Rs = 0.531, p < 0.00001). These results confirm that geomagnetic variations in the ULF range serve as a rhythm sensor for a healthy body under normal conditions. Being a “case study”, our results require further verification on large volumes of data in different geomagnetic conditions. Full article

(This article belongs to the Special Issue Novel Insights into the Effects of Space Weather on Human Health (2nd Edition))

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(a) Time series of Dst index on 24–27 September 2023. Green circles show experiments with Q > 0.4, red circles—Q < 0.4; and (b) distribution of the relative number of experiments with Q > 0.4 by date. Full article ">Figure 2
(a) Time series of Dst index on 10–15 May 2024. Green—Q > 0.4, red—Q < 0.4. Circles—Volunteer A, triangles—Volunteer B; and (b) distribution of the relative number of experiments with Q > 0.4 by date. Full article ">Figure 3
(a) Time series of Dst index on 10–13 October 2024. Green—Q > 0.4, red—Q < 0.4. Circles—Volunteer A, triangles—Volunteer B; and (b) distribution of the relative number of experiments with Q > 0.4 by date. Full article ">Figure 4
Examples of time series depicting the minute values of the H-component of the GMF observed at the Nurmijarvi (NURH, blue), Mocsow (MOSH, red) and Surlary (SUAH, green) geophysical stations after application of a band-pass filter. (a)—Experiment 11 May 2024, 16:37 UT + 3 h, Dst = −292 nT. (b)—Experiment 12 October 2024, 10:00 UT + 3 h, Dst = −71 nT. (c)—Experiment 13 October 2024, 11:00 UT + 3 h, Dst = −29 nT. Full article ">Figure 4 Cont.
Examples of time series depicting the minute values of the H-component of the GMF observed at the Nurmijarvi (NURH, blue), Mocsow (MOSH, red) and Surlary (SUAH, green) geophysical stations after application of a band-pass filter. (a)—Experiment 11 May 2024, 16:37 UT + 3 h, Dst = −292 nT. (b)—Experiment 12 October 2024, 10:00 UT + 3 h, Dst = −71 nT. (c)—Experiment 13 October 2024, 11:00 UT + 3 h, Dst = −29 nT. Full article ">Figure 5
An example of the wavelet transformation of the biological and geophysical time series in the experiment 12 October 2024, begin 10:00 (UT + 3 h): (a) wavelet image of HR series of Volunteer A; (b) wavelet image of HR series of Volunteer B; (c) wavelet image of GMF vector H; and (d–f) vectors [g] and [h] as a result of averaging the wavelet matrices of HR and MOSH series. Full article ">Figure 5 Cont.
An example of the wavelet transformation of the biological and geophysical time series in the experiment 12 October 2024, begin 10:00 (UT + 3 h): (a) wavelet image of HR series of Volunteer A; (b) wavelet image of HR series of Volunteer B; (c) wavelet image of GMF vector H; and (d–f) vectors [g] and [h] as a result of averaging the wavelet matrices of HR and MOSH series. Full article ">Figure 6
Scatter plots of the relationships between the Q values for the total sample of experiments and the parameters of the geomagnetic environment: (a) Rp(NUR-SUA); (b) Ampl(NURH); and (c) Dst index. Full article ">

15 pages, 6100 KiB

Open AccessArticle

The Characteristics and Possible Mechanisms of the Strongest Ionospheric Irregularities in March 2024

by Jinghua Li, Guanyi Ma, Jiangtao Fan, Qingtao Wan, Takashi Maruyama, Jie Zhang, Chi-Kuang Chao, Liang Dong, Dong Wang, Yang Gao and Le Zhang

Atmosphere 2025, 16(2), 218; https://doi.org/10.3390/atmos16020218 - 15 Feb 2025

Viewed by 734

Abstract

A geomagnetic storm occurred on 3 March 2024, with the minimum SYM-H reaching −127 nT. Although this geomagnetic storm was not very strong, the ionospheric irregularities on this day resulted in a strong ionospheric scintillation. The amplitude scintillation index was larger than 1.0. [...] Read more.

A geomagnetic storm occurred on 3 March 2024, with the minimum SYM-H reaching −127 nT. Although this geomagnetic storm was not very strong, the ionospheric irregularities on this day resulted in a strong ionospheric scintillation. The amplitude scintillation index was larger than 1.0. Global Navigation Satellite System (GNSS) receivers experienced numerous cycle slips and loss of lock of carrier phase over a large longitudinal range of ~30 degrees within ~5 h in the south of China. The occurrence of cycle slips over such a long duration and extensive longitudinal range is rarely reported. Ground-based GNSS receivers, ionosondes and in situ satellite measurements were utilized to analyze the characteristics of the equatorial plasma bubbles (EPBs) during this event. The EPBs began before the main phase of the geomagnetic storm and extended to 30°N in latitude. Possible physical mechanisms for the initial generation and the development of the EPBs are discussed. It is believed that different mechanisms played vital roles in the initial generation and development of the EPBs before and after the onset of the main phase of the geomagnetic storm. Moreover, a large-scale wave structure (LSWS) could potentially serve as the seeding source of the EPBs. Full article

(This article belongs to the Special Issue Ionospheric Irregularity (2nd Edition))

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18 pages, 6051 KiB

Open AccessArticle

Construction and Analysis of the Ecological Security Pattern in Territorial Space in Shaanxi of the Yellow River Basin, China

by Zhengyao Liu, Jing Huang, Xiaokang Liu, Yonghong Li and Yiping He

Atmosphere 2025, 16(2), 217; https://doi.org/10.3390/atmos16020217 - 14 Feb 2025

Viewed by 196

Abstract

In the context of rapid urbanization and extreme climate change globally, balancing ecological resources and economic development for land spatial planning has become one of the pressing issues that need to be addressed. This study proposes a composite model to construct a spatial [...] Read more.

In the context of rapid urbanization and extreme climate change globally, balancing ecological resources and economic development for land spatial planning has become one of the pressing issues that need to be addressed. This study proposes a composite model to construct a spatial ecological security pattern. It identifies restoration areas with different risk levels based on the spatial distribution of land use, offering suggestions for optimizing spatial configuration. Focusing on the central Shaanxi region of the Yellow River Basin in China, ecological sources are identified by integrating ecological factors, and ecological corridors and restoration zones are extracted using the minimum cumulative resistance difference and circuit theory. The results indicate significant improvements in ecological quality and desertification in the study area from 2000 to 2020. Currently, the core area covers 51,649.71 km², accounting for 62.18% of all landscape types; the total ecological source area covers 31,304.88 km², representing 18.84% of the entire area. These ecological source areas are mainly distributed in the northern Loess Plateau and the southern mountainous regions. The area has 26 important ecological corridors, identifying 16 ecological pinch points and 12 ecological barriers, presenting an ecological security pattern characterized by a grid-like structure in the northern region and a dispersed pattern in the southern region. Additionally, 273.72 km² of ecological restoration priority areas and 197.98 square kilometers of ecological restoration encouragement areas are proposed as key planning regions for ecological environmental protection. This study provides references for optimizing spatial configuration to promote the sustainable development of urban and rural living environments in the Yellow River Basin. Full article

(This article belongs to the Special Issue Desert Climate and Environmental Change: From Past to Present)

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15 pages, 6578 KiB

Open AccessArticle

Regionalization and Analysis of Precipitation Variations in Inner Mongolia

by Wei Wang and Jiao Guo

Atmosphere 2025, 16(2), 216; https://doi.org/10.3390/atmos16020216 - 14 Feb 2025

Viewed by 207

Abstract

Precipitation data from 104 meteorological stations in Inner Mongolia from 1960 to 2018 were analyzed to examine the regionalization and characteristics of precipitation variations. Using rotated empirical orthogonal function (REOF) analysis and K-means clustering, Inner Mongolia was divided into six precipitation subregions: the [...] Read more.

Precipitation data from 104 meteorological stations in Inner Mongolia from 1960 to 2018 were analyzed to examine the regionalization and characteristics of precipitation variations. Using rotated empirical orthogonal function (REOF) analysis and K-means clustering, Inner Mongolia was divided into six precipitation subregions: the northeastern Hulunbuir area (subregion I); most of Hinggan League, northern Xilin Gol League, and northwestern Tongliao City (subregion II); most of Tongliao City and Chifeng City and east–central and southern Xilin Gol League (subregion III); southern Xilin Gol League, north–central Ulan Chab City, northern Hohhot City, most of Baotou City and north–central Bayannur City (subregion IV); Ordos City, southern Bayannur, and southeastern Alxa League (subregion V); and west–central Alxa League and parts of western Bayannur City (subregion VI). Precipitation showed a spatial gradient with higher annual averages in the east (400.85 mm in subregion I) and lower averages in the west (90.65 mm in subregion VI). From 1960 to 2018, precipitation exhibited an overall increasing trend consistent across the subregions. However, most regions showed decreasing trends from 1990 to 2010. The rate of precipitation change varied significantly across the subregions, reflecting distinct spatial dynamics. Full article

(This article belongs to the Section Meteorology)

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19 pages, 5257 KiB

Open AccessArticle

Application of Short-Term Measurements to Estimate the Annual Mean Indoor Air Radon-222 Activity Concentration

by Franz Josef Maringer and Marius Blum

Atmosphere 2025, 16(2), 215; https://doi.org/10.3390/atmos16020215 - 14 Feb 2025

Viewed by 233

Abstract

A method was developed to estimate the average annual indoor radon activity concentration from three-week short-term measurements using active radon-222 measuring devices, taking into account the relevant influencing parameters (season, temperature difference, temporal air pressure gradient, etc.) during the short-term measurements. A total [...] Read more.

A method was developed to estimate the average annual indoor radon activity concentration from three-week short-term measurements using active radon-222 measuring devices, taking into account the relevant influencing parameters (season, temperature difference, temporal air pressure gradient, etc.) during the short-term measurements. A total of 24 long-term measurements (6 months) and 50 short-term measurements (3 weeks) were carried out in 24 indoor spaces in private houses in four Austrian federal states between October 2022 and July 2023. At the same time as the short-term measurements, ambient parameters (outside and inside temperature, air pressure inside, outside, air humidity inside, outside, wind speed, wind direction, amount of precipitation) were also recorded to investigate their influence on the measured radon-222 activity concentrations. Building and usage data of the indoor spaces examined were also collected. Based on the evaluation of the radon-222 measurements carried out, a first guideline was developed for estimating the annual mean value of the radon-222 activity concentration from short-term measurements lasting around three weeks. The result shows that by applying the developed method, the approximation to the long-term average value can be significantly improved, at least by a factor of 2. This criterion is only valid for the 24 indoor spaces examined in this study. Generalisation requires a test and validation study of the method presented. It is planned to test and validate the developed method in other indoor spaces by means of further measurements and in-depth physical-statistical considerations, and to improve the functional relationships and the approximation to the long-term average value. Full article

(This article belongs to the Special Issue Atmospheric Radon Concentration Monitoring and Measurements (2nd Edition))

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Locations of the measured sites in Austria—(a) Upper Austria, (b) northern Lower Austria and Vienna, (c) southern Lower Austria, and (d) Tyrol (maps: Wikimedia Commons). Full article ">Figure 2
Instruments comparison: course of the devices’ given hourly average values at higher Rn-222 activity concentrations. Full article ">Figure 3
Course of SIRC, Formula (3), over the course of a year (for EIRC = 300 Bq/m3). Full article ">Figure 4
Dependence of SIRC, Formula (4), on the outside temperature TTX* (for EIRC = 300 Bq/m3). Full article ">Figure 5
Dependence, Formula (9), of SIRC on the relative standard deviation of measured cRn values (for EIRC = 300 Bq/m3). Full article ">Figure 6
Dependence, Formula (10), of SIRC on the mean value of all PPX′ values < 0 (for EIRC = 300 Bq/m3). Full article ">Figure 7
Deviations (SIRC (short-term measurement)/LIRC (long-term measurement)) − 1 (red) and (EIRC (annual mean estimated value)/LIRC (long-term measurement)) − 1 (green) of the individual measurements (time of measurement = mid-way through the measurement period; uncertainty bars ±3% to ±11% omitted for clarity). Full article ">Figure 8
Deviations (with uncertainty bars) (SIRC/LIRC) − 1 (red) and (EIRC/LIRC) − 1 (green)—sorted in ascending order, and LIRC (Bq/m3) of the long-term measurements (orange). Full article ">

19 pages, 5449 KiB

Open AccessArticle

Space-Based Limb-Imaging Spectrometer for Atmospheric O₂ Airglow Detection

by Minjie Zhao, Haijin Zhou, Yu Jiang, Shuhua Huang, Xin Zhao, Yi Zeng, Jun Chen, Fenglei Liu, Xiaohan Qiu, Quan Zhang, Lei Zhu, Shimei Wang, Kai Zhan, Ge Yan and Fuqi Si

Atmosphere 2025, 16(2), 214; https://doi.org/10.3390/atmos16020214 - 13 Feb 2025

Viewed by 363

Abstract

This paper presents a space-based limb-imaging spectrometer (LIS) for detecting atmospheric O₂ airglow; it scans the atmosphere with a vertical range of 10–100 km and has a vertical resolution of 2 km. The LIS’s detection performance needs to be examined before launch. [...] Read more.

This paper presents a space-based limb-imaging spectrometer (LIS) for detecting atmospheric O₂ airglow; it scans the atmosphere with a vertical range of 10–100 km and has a vertical resolution of 2 km. The LIS’s detection performance needs to be examined before launch. A forward radiative transfer model (RTM) of airglow is studied to determine the airglow emission intensity. Spectral and radiation calibration is conducted to obtain the response parameters. Based on the airglow emission intensity, calibration results, and airglow spectral lines, the LIS’s simulated spectra are obtained, and then an optimal estimation inversion method for the LIS is studied. The results show that the LIS’s spectral range is 498.1 nm–802.3 nm, with a spectral resolution of 1.38 nm. Simulation results show that the LIS can detect airglow emission spectral lines, which characterize their dependence on temperature. The digital number response value is 20% to 50% of the saturation value. An inversion error analysis shows that, when the signal-to-noise ratio (SNR) of the LIS is 1000 and the prior temperature error is 10%, the inversion errors are 6.2 and 3 K at 63 and 77 km, respectively. This study shows that the LIS can achieve good SNR detection for airglow. Full article

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

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24 pages, 9886 KiB

Open AccessArticle

Comparison of Machine Learning Algorithms for Daily Runoff Forecasting with Global Rainfall Products in Algeria

by Rayane Bounab, Hamouda Boutaghane, Tayeb Boulmaiz and Yves Tramblay

Atmosphere 2025, 16(2), 213; https://doi.org/10.3390/atmos16020213 - 13 Feb 2025

Viewed by 289

Abstract

Rainfall–runoff models are crucial tools for managing water resources. The absence of reliable rainfall data in many regions of the world is a major limitation for these models, notably in many African countries, although some recent global rainfall products can effectively monitor rainfall [...] Read more.

Rainfall–runoff models are crucial tools for managing water resources. The absence of reliable rainfall data in many regions of the world is a major limitation for these models, notably in many African countries, although some recent global rainfall products can effectively monitor rainfall from space. In Algeria, to identify a relevant modeling approach using this new source of rainfall information, the present research aims to (i) compare a conceptual model (GR4J) and seven machine learning algorithms (FFNN, ELM, LSTM, LSTM2, GRU, SVM, and GPR) and (ii) compare different types of precipitation inputs, including four satellite products (CHIRPS, SM2RAIN, GPM, and PERSIANN), one reanalysis product (ERA5), and observed precipitation, to assess which combination of models and precipitation data provides the optimal performance for river discharge simulation. The results show that the ELM, FFNN, and LSTM algorithms give the best performance (NSE > 0.6) for river runoff simulation and provide reliable alternatives compared to a conceptual hydrological model. The SM2RAIN-ASCAT and ERA5 rainfall products are as efficient as observed precipitation in this data-scarce context. Consequently, this work is the first step towards the implementation of these tools for the operational monitoring of surface water resources in Algeria. Full article

(This article belongs to the Section Meteorology)

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12 pages, 1465 KiB

Open AccessArticle

Assessment of BTEX, PM₁₀, and PM_2.5 Concentrations in Nakhon Pathom, Thailand, and the Health Risks for Security Guards and Copy Shop Employees

by Navaporn Kanjanasiranont

Atmosphere 2025, 16(2), 212; https://doi.org/10.3390/atmos16020212 - 13 Feb 2025

Viewed by 322

Abstract

Concentrations of PM₁₀, PM_2.5, and the BTEX chemical group were studied in Nakhon Pathom, Thailand. The occupational health risk for workers (security guards and printing machine operators) was estimated against exposure to these pollutants. The average levels of PM [...] Read more.

Concentrations of PM₁₀, PM_2.5, and the BTEX chemical group were studied in Nakhon Pathom, Thailand. The occupational health risk for workers (security guards and printing machine operators) was estimated against exposure to these pollutants. The average levels of PM₁₀, PM_2.5, and BTEX (benzene, toluene, ethylbenzene, and xylenes) were 67.32, 40.21, and 80.93 µg/m³, respectively. Among the BTEX group, toluene was the most prevalent at all the sampling sites, with mean levels of 55.71 µg/m³. The measured toluene/benzene ratios (T/B) indicated that the potential sources of BTEX at EG, CP1, and CP2 sites were influenced by vehicular or traffic sources. The level of benzene was utilized for evaluating the risk of cancer, whereas toluene and PM_2.5 were estimated for non-cancer health risk. According to the health risk assessment (at the 95% CI), security guards tended to have higher cancer risk values due to benzene (4.04 × 10⁻⁵) when compared to printing machine operators (2.41 × 10⁻⁵) due to their frequent exposure to particular sources of high concentration. Meanwhile, the non-cancer risk values were at an acceptable level for security guards and copy center employees. In order to lower the overall cancer risk levels of workers, the most effective method is to reduce the chemical concentration. Full article

(This article belongs to the Special Issue Air Pollution: Health Risks and Mitigation Strategies)

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30 pages, 32662 KiB

Open AccessArticle

Air Pollution Trends and Predictive Modeling for Three Cities with Different Characteristics Using Sentinel-5 Satellite Data and Deep Learning

by Salma Alkayal, Hind Almisbahi, Souad Baowidan and Entisar Alkayal

Atmosphere 2025, 16(2), 211; https://doi.org/10.3390/atmos16020211 - 13 Feb 2025

Viewed by 329

Abstract

Accurate air quality forecasting is important in pollution prevention and risk reduction. Effective short-term and long-term forecasting models are needed. This study investigated the need for a new model to forecast air pollution concentrations in three cities with distinct characteristics: a city with [...] Read more.

Accurate air quality forecasting is important in pollution prevention and risk reduction. Effective short-term and long-term forecasting models are needed. This study investigated the need for a new model to forecast air pollution concentrations in three cities with distinct characteristics: a city with high industrial activity, a city with a high population density and urbanization, and an agricultural city. The air pollution data were collected using the Sentinel-5P satellite and Google Earth Engine to apply descriptive analysis and comparison of two years, 2022 and 2023. The studied cities were Al Riyadh (high population), Al Jubail (industrial), and Najran (agricultural) in Saudi Arabia. The selected pollutants were SO₂, NO₂, CO, O₃, and HCHO. In addition, this study investigated the variations observed in all the pollutants during the months of the year, the correlations between the contaminants, and the correlation between NO₂ and the meteorological data. Based on our findings, Al Jubail had the highest level of all the pollutants during the two years, except for NO₂, for which the highest level was observed in Al Riyadh, which has witnessed notable urbanization and development recently. Moreover, this study developed a forecasting model for the concentration of NO₂ based on weather data and the previous values of NO₂ using Long Short-Term Memory (LSTM) and Time2Vec. The modeling proved that any model that is trained on data collected from a specific city is not suitable for predicting the pollution level in another city and the level of another pollutant, as the three cities have different correlations with the pollutants and the weather data. The proposed model demonstrated a superior accuracy in predicting NO₂ concentrations compared to traditional LSTM models, effectively capturing temporal patterns and achieving minimal prediction errors, which contributes to ongoing efforts to understand the dynamics of air pollution based on cities’ characteristics and the period of the year. Full article

(This article belongs to the Special Issue Dispersion and Mitigation of Atmospheric Pollutants)

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22 pages, 6074 KiB

Open AccessArticle

Research on the Attenuation Characteristics of LiDAR Transmission Energy in Different Atmospheric Environments

by Xiaoce Yang, Chunyang Wang and Xuelian Liu

Atmosphere 2025, 16(2), 210; https://doi.org/10.3390/atmos16020210 - 12 Feb 2025

Viewed by 358

Abstract

LiDAR, as a novel detection system, has found extensive applications across diverse industries. However, when lasers propagate through the atmosphere, the energy undergoes significant attenuation due to various environmental factors, thereby impeding the performance of LiDAR systems. This paper focuses on analyzing the [...] Read more.

LiDAR, as a novel detection system, has found extensive applications across diverse industries. However, when lasers propagate through the atmosphere, the energy undergoes significant attenuation due to various environmental factors, thereby impeding the performance of LiDAR systems. This paper focuses on analyzing the distribution patterns of fog particles, haze particles, and typical aerosol particles within the atmospheric environment. By integrating Mie scattering theory, it delves into the absorption and scattering behaviors exhibited by different atmospheric constituents. Employing numerical simulation techniques, the attenuation characteristics of the 1064 nm working-wavelength laser under the influence of diverse particles are simulated and scrutinized. In conjunction with the LiDAR transmission equation, the attenuation law governing the transmission energy of the laser under varying atmospheric conditions is also analyzed. The results reveal that atmospheric pollutant particles such as fog particles, haze particles, dust particles, and bituminous coal particles all contribute to energy attenuation during laser transmission. Notably, bituminous coal particles induce the most severe attenuation. Full article

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

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27 pages, 10948 KiB

Open AccessArticle

The Role of Atmospheric Circulation Patterns in Water Storage of the World’s Largest High-Altitude Landslide-Dammed Lake

by Xuefeng Deng, Yizhen Li, Jingjing Zhang, Lingxin Kong, Jilili Abuduwaili, Majid Gulayozov, Anvar Kodirov and Long Ma

Atmosphere 2025, 16(2), 209; https://doi.org/10.3390/atmos16020209 - 12 Feb 2025

Viewed by 282

Abstract

This study reconstructed the annual lake surface area (LSA) and absolute lake water storage (LWS) changes of Lake Sarez, the world’s largest high-altitude landslide-dammed lake, from 1992 to 2023 using multi-source remote sensing data. All available Landsat images were used to extract the [...] Read more.

This study reconstructed the annual lake surface area (LSA) and absolute lake water storage (LWS) changes of Lake Sarez, the world’s largest high-altitude landslide-dammed lake, from 1992 to 2023 using multi-source remote sensing data. All available Landsat images were used to extract the LSA using an improved multi-index threshold method, which incorporates a slope mask and threshold adjustment to enhance the boundary delineation accuracy (Kappa coefficient = 0.94). By combining the LSA with high-resolution DEM and the GLOBathy bathymetry dataset, the absolute LWS was reconstructed, fluctuating between 12.3 × 10⁹ and 12.8 × 10⁹ m³. A water balance analysis revealed that inflow runoff (IRO) was the primary driver of LWS changes, contributing 54.57%. The cross-wavelet transform and wavelet coherence analyses showed that the precipitation (PRE) and snow water equivalent (SWE) were key climatic factors that directly influenced the variability of IRO, impacting the interannual water availability in the lake, with PRE having a more sustained impact. Temperature indirectly regulated IRO by affecting SWE and potential evapotranspiration. Furthermore, IRO exhibited different resonance periods and time lags with various atmospheric circulation factors, with the Pacific Decadal Oscillation and North Atlantic Oscillation having the most significant influence on its interannual variations. These findings provide crucial insights into the hydrological behavior of Lake Sarez under climate change and offer a novel approach for studying water storage dynamics in high-altitude landslide-dammed lakes, thereby supporting regional water resource management and ecological conservation. Full article

(This article belongs to the Section Meteorology)

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15 pages, 20002 KiB

Open AccessArticle

Study on the CSES Electric Field VLF Electromagnetic Pulse Sequences Triggered by Volcanic Eruptions

by Siyu Liu, Ying Han, Qingjie Liu, Jianping Huang, Zhong Li and Xuhui Shen

Atmosphere 2025, 16(2), 208; https://doi.org/10.3390/atmos16020208 - 12 Feb 2025

Viewed by 320

Abstract

Volcanic eruptions, as a natural phenomenon, can generate electric field disturbances that may interfere with the ionosphere, potentially impacting communication systems and electronic devices. This paper conducts identification and classification analyses of the electromagnetic pulse (EMP) disturbances in the very low frequency (VLF) [...] Read more.

Volcanic eruptions, as a natural phenomenon, can generate electric field disturbances that may interfere with the ionosphere, potentially impacting communication systems and electronic devices. This paper conducts identification and classification analyses of the electromagnetic pulse (EMP) disturbances in the very low frequency (VLF) electric field data observed by the China Seismo-Electromagnetic Satellite (CSES) and finds that volcanic eruption events can trigger EMP sequences. This paper first applies Fourier transform to the VLF electric field waveform data to convert it into 4 s spectrograms. Then, a series of operations such as grayscale conversion, edge feature enhancement, and binarization are performed on the spectrograms. Subsequently, a K-means clustering algorithm is applied to the binarized results to identify EMP events on the spectrograms. Finally, a classification analysis is performed on the identified results, revealing that multiple volcanic eruption events generate EMP sequences. The results of this paper not only provide new insights into the impact of volcanic eruptions on the electromagnetic environment but also have significant implications for enhancing the anti-interference capability of communication systems and optimizing electromagnetic environment monitoring technologies. Full article

(This article belongs to the Section Upper Atmosphere)

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22 pages, 11030 KiB

Open AccessArticle

Adjusting Soil Temperatures with a Physics-Informed Deep Learning Model for a High-Resolution Numerical Weather Prediction System

by Qiufan Wang, Yubao Liu, Yueqin Shi and Shaofeng Hua

Atmosphere 2025, 16(2), 207; https://doi.org/10.3390/atmos16020207 - 12 Feb 2025

Viewed by 309

Abstract

Soil temperature (ST) plays an important role in the surface heat energy balance, and an accurate description of soil temperatures is critical for numerical weather prediction; however, it is difficult to consistently measure soil temperatures. We developed a U-Net-based deep learning model to [...] Read more.

Soil temperature (ST) plays an important role in the surface heat energy balance, and an accurate description of soil temperatures is critical for numerical weather prediction; however, it is difficult to consistently measure soil temperatures. We developed a U-Net-based deep learning model to derive soil temperatures (designated as ST-U-Net) primarily based on 2 m air temperature (T2) forecasts. The model, the domain of which covers the Mt. Lushan region, was trained and tested by utilizing the high-resolution forecast archive of an operational weather research and forecasting four-dimensional data assimilation (WRF-FDDA) system. The results showed that ST-U-Net can accurately estimate soil temperatures based on T2 inputs, achieving a mean absolute error (MAE) of less than 0.8 K on the testing set of 5055 samples. The performance of ST-U-Net varied diurnally, with smaller errors at night and slightly larger errors in the daytime. Incorporating additional inputs such as land uses, terrain height, radiation flux, surface heat flux, and coded time further reduced the MAE for ST by 26.7%. By developing a boundary-layer physics-guided training strategy, the error was further reduced by 8.8%. Full article

(This article belongs to the Section Meteorology)

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Atmosphere, Volume 16, Issue 2 (February 2025) – 123 articles

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