Atmosphere

25 pages, 4564 KiB

Open AccessArticle

Harnessing Deep Learning and Snow Cover Data for Enhanced Runoff Prediction in Snow-Dominated Watersheds

by Rana Muhammad Adnan, Wang Mo, Ozgur Kisi, Salim Heddam, Ahmed Mohammed Sami Al-Janabi and Mohammad Zounemat-Kermani

Atmosphere 2024, 15(12), 1407; https://doi.org/10.3390/atmos15121407 - 22 Nov 2024

Abstract

Predicting streamflow is essential for managing water resources, especially in basins and watersheds where snowmelt plays a major role in river discharge. This study evaluates the advanced deep learning models for accurate monthly and peak streamflow forecasting in the Gilgit River Basin. The [...] Read more.

Predicting streamflow is essential for managing water resources, especially in basins and watersheds where snowmelt plays a major role in river discharge. This study evaluates the advanced deep learning models for accurate monthly and peak streamflow forecasting in the Gilgit River Basin. The models utilized were LSTM, BiLSTM, GRU, CNN, and their hybrid combinations (CNN-LSTM, CNN-BiLSTM, CNN-GRU, and CNN-BiGRU). Our research measured the model’s accuracy through root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), and the coefficient of determination (R²). The findings indicated that the hybrid models, especially CNN-BiGRU and CNN-BiLSTM, achieved much better performance than traditional models like LSTM and GRU. For instance, CNN-BiGRU achieved the lowest RMSE (71.6 in training and 95.7 in testing) and the highest R² (0.962 in training and 0.929 in testing). A novel aspect of this research was the integration of MODIS-derived snow-covered area (SCA) data, which enhanced model accuracy substantially. When SCA data were included, the CNN-BiLSTM model’s RMSE improved from 83.6 to 71.6 during training and from 108.6 to 95.7 during testing. In peak streamflow prediction, CNN-BiGRU outperformed other models with the lowest absolute error (108.4), followed by CNN-BiLSTM (144.1). This study’s results reinforce the notion that combining CNN’s spatial feature extraction capabilities with the temporal dependencies captured by LSTM or GRU significantly enhances model accuracy. The demonstrated improvements in prediction accuracy, especially for extreme events, highlight the potential for these models to support more informed decision-making in flood risk management and water allocation. Full article

(This article belongs to the Special Issue Novel Algorithms and Advanced Computing Methods Application in Atmosphere)

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28 pages, 26490 KiB

Open AccessArticle

Vertical Accelerations and Convection Initiation in an Extreme Precipitation Event in the Western Arid Areas of Southern Xinjiang

by Na Li, Lingkun Ran, Daoyong Yang, Baofeng Jiao, Cha Yang, Wenhao Hu, Qilong Sun and Peng Tang

Atmosphere 2024, 15(12), 1406; https://doi.org/10.3390/atmos15121406 - 22 Nov 2024

Abstract

A simulation of an extreme precipitation event in southern Xinjiang, which is the driest area in China, seizes the whole initiation process of the intense convective cell responsible for the high hourly rainfall amount. Considering the inner connection between convection and vertical motions, [...] Read more.

A simulation of an extreme precipitation event in southern Xinjiang, which is the driest area in China, seizes the whole initiation process of the intense convective cell responsible for the high hourly rainfall amount. Considering the inner connection between convection and vertical motions, the characteristics and mechanisms of the vertical accelerations during this initial development of the deep convection are studied. It is shown that three key accelerations are responsible for the development from the nascent cumuli to a precipitating deep cumulonimbus, including sub-cloud boundary-layer acceleration, in-cloud deceleration, and cloud-top acceleration. By analyzing the right-hand terms of the vertical velocity equation in the framework of the WRF model, together with a diagnosed relation of perturbation pressure to perturbation potential temperature, perturbation-specific volume (or density), and moisture, the physical processes associated with the corresponding accelerations are revealed. It is found that sub-cloud acceleration is associated with three-dimensional divergence, indicating that the amount of upward transported air must be larger than that of horizontally convergent air. This is favorable for the persistent accumulation of water vapor into the accelerated area. In-cloud deceleration is caused by the intrusion or entrainment of mid-level cold air, which cools down the developing cloud and delays the deep convection formation. Cloud-top acceleration is responsible for the rapid upward extension of the cloud top, which is highly correlated with the convergence and upward transport of moisture. Full article

(This article belongs to the Section Meteorology)

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13 pages, 876 KiB

Open AccessArticle

Impact of Air Pollution on the Long-Term Decline of Non-Idiopathic Pulmonary Fibrosis Interstitial Lung Disease

by Pablo Mariscal-Aguilar, Luis Gómez-Carrera, Gema Bonilla, Carlos Carpio, Ester Zamarrón, María Fernández-Velilla, Mariana Díaz-Almirón, Francisco Gayá, Elena Villamañán, Concepción Prados and Rodolfo Álvarez-Sala

Atmosphere 2024, 15(12), 1405; https://doi.org/10.3390/atmos15121405 - 22 Nov 2024

Abstract

Objective: This study examines the association between major urban pollutants and the long-term decline of non-idiopathic pulmonary fibrosis interstitial lung disease [non-IPF ILD]. Materials and methods: A total of 41 patients with non-IPF ILD were analyzed from 2011 to 2020, correlating disease long-term [...] Read more.

Objective: This study examines the association between major urban pollutants and the long-term decline of non-idiopathic pulmonary fibrosis interstitial lung disease [non-IPF ILD]. Materials and methods: A total of 41 patients with non-IPF ILD were analyzed from 2011 to 2020, correlating disease long-term decline with concentrations of key pollutants [SO₂, CO, NO₂, O₃, PM_2.5, and PM₁₀] in Madrid. The likelihood of meeting severity criteria was assessed using a generalized linear model, considering the average pollutant levels during severe episodes. Results: At diagnosis, the average age of patients was 62.95 ± 13.13 years, with 47.6% women. The study found no significant association between pollution levels and the probability of meeting severity criteria for non-IPF ILD. The odds ratios were as follows: OR SO₂ = 0.92 [0.82–1.03], p = 0.16; OR CO = 0.99 [0.97–1.05], p = 0.70; OR NO₂ = 0.97 [0.92–1.03], p = 0.38; OR PM_2.5 = 0.79 [0.54–1.17], p = 0.24; OR PM₁₀ = 1.1 [0.94–1.28], p = 0.21; OR O₃ = 0.97 [0.92–1.01], p = 0.20. Conclusions: Our study suggests that, within the cohort of 41 patients with non-IPF ILD enrolled in this study, urban air pollutants in Madrid are not significantly linked to increased long-term decline of non-IPF ILD. This is one of the first studies to explore the impact of various urban pollutants on a diverse cohort of non-IPF ILD patients, including rare conditions like LAM and histiocytosis X. Further research with larger sample sizes and comprehensive exposure assessments is recommended. Full article

(This article belongs to the Special Issue Air Pollution Exposure and Health Impact Assessment (2nd Edition))

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11 pages, 2501 KiB

Open AccessArticle

Impact of Crop Type and Soil Characteristics on Greenhouse Gas Emissions in Latvian Agricultural Systems

by Karlis Memgaudis, Jovita Pilecka-Ulcugaceva and Kristine Valujeva

Atmosphere 2024, 15(12), 1404; https://doi.org/10.3390/atmos15121404 - 22 Nov 2024

Abstract

This study investigates the impact of crop type and soil characteristics on greenhouse gas (GHG) emissions in Latvian agriculture, offering insights directly relevant to policymakers and practitioners focused on sustainable land management. From 2020 to 2023, emissions were monitored across four agricultural sites [...] Read more.

This study investigates the impact of crop type and soil characteristics on greenhouse gas (GHG) emissions in Latvian agriculture, offering insights directly relevant to policymakers and practitioners focused on sustainable land management. From 2020 to 2023, emissions were monitored across four agricultural sites featuring different crop rotations: blueberry monoculture, continuous maize cropping, winter barley–winter rapeseed rotation, and spring barley–bean–winter wheat–fallow rotation. Results indicate that GHG emissions vary widely depending on crop and soil type. CO₂ emissions varied significantly based on both crop and soil type, with organic soils under maize cultivation in Mārupe averaging 184.91 kg CO₂ ha⁻¹ day⁻¹, while mineral soils in Bērze under spring barley emitted 60.98 kg CO₂ ha⁻¹ day⁻¹. Methane absorption was highest in well-aerated mineral soils, reaching 6.11 g CH₄ ha⁻¹ day⁻¹ in spring barley fields in Auce. Maize cultivation contributed the highest N₂O emissions, reaching 33.15 g N₂O ha⁻¹ day⁻¹. These findings underscore that targeted practices, like optimized crop rotation and fertilizer use, can substantially reduce GHG emissions. Climate variability across locations affects soil moisture and temperature, but these factors were statistically controlled to isolate the impacts of crop type and soil characteristics on emissions. This study provides valuable data to inform sustainable agricultural policies and help achieve EU climate goals. Full article

(This article belongs to the Special Issue Gas Emissions from Soil)

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17 pages, 871 KiB

Open AccessSystematic Review

The Impact of Outdoor Environmental Factors on Indoor Air Quality in Education Settings: A Systematic Review

by Jan Rožanec, An Galičič and Andreja Kukec

Atmosphere 2024, 15(12), 1403; https://doi.org/10.3390/atmos15121403 - 22 Nov 2024

Abstract

Poor indoor air quality (IAQ) in schools is associated with pupils’ health and their learning performance. This study aims to provide an overview of the outdoor factors that affect the IAQ in educational settings in order to develop public health measures. We conducted [...] Read more.

Poor indoor air quality (IAQ) in schools is associated with pupils’ health and their learning performance. This study aims to provide an overview of the outdoor factors that affect the IAQ in educational settings in order to develop public health measures. We conducted a systematic literature review to investigate the outdoor factors that affect IAQ in educational settings. The selection of articles included 17,082 search string hits from the ScienceDirect database published between 2010 and 2023, with 92 relevant studies selected based on the inclusion and exclusion criteria. Based on a systematic review of the literature, we identified the following outdoor factors: proximity to busy roads, commercial and industrial establishments, meteorological conditions, compounds from the natural environment, emissions from heating buildings, atmospheric reactions and secondary pollutants, unpaved school playgrounds, and smoking. This study provides key information on the mentioned outdoor factors and gives recommendations on measures to reduce classroom pollutant concentrations while highlighting educational settings that require special attention. Our study shows that classroom IAQ is affected by many outdoor pollutant sources, the prevalence of which depends on the educational setting’s micro location. Therefore, it is essential to develop an appropriate classroom ventilation strategy for each educational setting. Full article

(This article belongs to the Section Air Quality)

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14 pages, 1503 KiB

Open AccessArticle

Determination of Polycyclic Aromatic Hydrocarbons from Atmospheric Deposition in Malva sylvestris Leaves Using Gas Chromatography with Mass Spectrometry (GC-MS)

by Giuseppe Ianiri, Alessandra Fratianni, Pasquale Avino and Gianfranco Panfili

Atmosphere 2024, 15(12), 1402; https://doi.org/10.3390/atmos15121402 - 22 Nov 2024

Abstract

Plant leaves can be used to determine the atmospheric deposition of organic contaminants, including polycyclic aromatic hydrocarbons (PAHs), to assess the contamination status of an area. The purpose of this study was to develop an analytical method for the determination of PAHs deriving [...] Read more.

Plant leaves can be used to determine the atmospheric deposition of organic contaminants, including polycyclic aromatic hydrocarbons (PAHs), to assess the contamination status of an area. The purpose of this study was to develop an analytical method for the determination of PAHs deriving from atmospheric deposition using Malva sylvestris leaves. Analytes were recovered from the leaves of the plant using cyclohexane as an organic solvent and subsequent sonication. The percentage recoveries (R%) were good (from 65.8 ± 3.2 to 104.2 ± 16.9), together with the instrumental analytical parameters, including correlation coefficients (r) ≥ 0.995 for all PAHs. The instrumental analysis was carried out using GC-MS in total ion current and single ion monitoring at the same time. Real samples taken from urban environments have shown that they are not always the most contaminated. At the Palermo site, leaves were observed to have high amounts of PAHs due to the deposition of dust generated by combustion processes that occurred near the sampling site. Further studies are recommended to compare the use of plants and classical sampling systems for monitoring the atmospheric deposition of key contaminants toxic to human health. Full article

(This article belongs to the Special Issue From Traditional to Emerging Air Pollutants: Tools and Health Risk Assessment)

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25 pages, 5612 KiB

Open AccessArticle

Innovative Approaches to Industrial Odour Monitoring: From Chemical Analysis to Predictive Models

by Claudia Franchina, Amedeo Manuel Cefalì, Martina Gianotti, Alessandro Frugis, Corrado Corradi, Giulio De Prosperis, Dario Ronzio, Luca Ferrero, Ezio Bolzacchini and Domenico Cipriano

Atmosphere 2024, 15(12), 1401; https://doi.org/10.3390/atmos15121401 - 21 Nov 2024

Abstract

This study evaluated the reliability of an electronic nose in monitoring odour concentration near a wastewater treatment plant and examined the correlation between four sensor readings and odour intensity. The electronic nose chemical sensors are related to the concentration of the following chemical [...] Read more.

This study evaluated the reliability of an electronic nose in monitoring odour concentration near a wastewater treatment plant and examined the correlation between four sensor readings and odour intensity. The electronic nose chemical sensors are related to the concentration of the following chemical species: two values for the concentration of VOCs recorded via the PID sensor (VPID) and the EC sensor (VEC), and concentrations of sulfuric acid (VH₂S) and benzene (VC₆H₆). Using Random Forest and least squares regression analysis, the study identifies VH₂S and VC₆H₆ as key contributors to odour concentration (C^c_OD). Three Random Forest models (RF0, RF1, RF2), with different characteristics for splitting between the test set and the training set, were tested, with RF1 showing superior predictive performance due to its training approach. All models highlighted VH₂S and VC₆H₆ as significant predictors, while VPID and VEC had less influence. A significant correlation between odour concentration and specific chemical sensor readings was found, particularly for VH₂S and VC₆H₆. However, predicting odour concentrations below 1000 ou_E/m³ proved challenging. Linear regression further confirmed the importance of VH₂S and VC₆H₆, with a moderate R-squared value of 0.70, explaining 70% of the variability in odour concentration. The study demonstrated the effectiveness of combining Random Forest and least squares regression for robust and interpretable results. Future research should focus on expanding the dataset and incorporating additional variables to enhance model accuracy. The findings underscore the necessity of specific sensor training and standardised procedures for accurate odour monitoring and characterisation. Full article

(This article belongs to the Special Issue Environmental Odour (2nd Edition))

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13 pages, 3613 KiB

Open AccessArticle

Impact of Large-Scale Circulations on Ground-Level Ozone Variability over Eastern China

by Jinlan Li and Ying Li

Atmosphere 2024, 15(12), 1400; https://doi.org/10.3390/atmos15121400 - 21 Nov 2024

Abstract

The seasonal and interannual variations in ground-level ozone across eastern China from 2014 to 2022 were strongly influenced by meteorological conditions and large-scale atmospheric circulations. We applied empirical orthogonal function (EOF) and singular value decomposition (SVD) analyses to explore these relationships. The EOF [...] Read more.

The seasonal and interannual variations in ground-level ozone across eastern China from 2014 to 2022 were strongly influenced by meteorological conditions and large-scale atmospheric circulations. We applied empirical orthogonal function (EOF) and singular value decomposition (SVD) analyses to explore these relationships. The EOF analysis identified three primary patterns of ozone variability: a dominant seasonal cycle over most of mainland China, an anti-correlation between northern and southern China during transitional seasons, and elevated springtime ozone concentrations in coastal regions. The SVD results further demonstrated that seasonal ozone variability was primarily driven by the annual radiation cycle across much of China. In contrast, the East Asian summer monsoon (EASM) was linked to the relatively low summer ozone levels observed in southern China. The anti-correlation between northern and southern China was associated with western Pacific subtropical high (WPSH) movement, which promoted sunny weather conditions and was conducive to ozone formation. Additionally, high springtime ozone levels in northern coastal regions were influenced by pollutant transport from continental cold high (CCH) events, while the cloud-free conditions and intense solar radiation in southern China contributed to elevated ozone concentrations. Full article

(This article belongs to the Section Air Quality)

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16 pages, 31264 KiB

Open AccessArticle

Spatiotemporal Variation Characteristics of Extreme Precipitation in Henan Province Based on RClimDex Model

by Zhijia Gu, Yuemei Li, Mengchen Qin, Keke Ji, Qiang Yi, Panying Li and Detai Feng

Atmosphere 2024, 15(11), 1399; https://doi.org/10.3390/atmos15111399 - 20 Nov 2024

Abstract

Global warming has led to an increasing frequency and intensity of extreme precipitation events worldwide. The extreme precipitation of Henan Province in central China usually occurs in summer, with the climate transition from the northern subtropical to the warm temperate climate. Compared with [...] Read more.

Global warming has led to an increasing frequency and intensity of extreme precipitation events worldwide. The extreme precipitation of Henan Province in central China usually occurs in summer, with the climate transition from the northern subtropical to the warm temperate climate. Compared with the study of extreme precipitation events in other regions, the study of Henan Province pays less attention. In order to systematically understand the spatial and temporal characteristics of extreme precipitation in Henan Province, this study applied RClimDex model to obtain nine extreme precipitation indices based on daily precipitation data from 90 meteorological stations from 1981 to 2020. Linear propensity estimation, M-K mutation test, Morlet wavelet analysis, and geostatistical analysis were used to investigate the spatial and temporal variation characteristics of the extreme precipitation indices in the region. The results indicated that continuous dry days (CDD), number of heavy rain days (R20mm), maximum daily precipitation (Rx1day), maximum precipitation for 5 consecutive days (Rx5day), and precipitation intensity (SDII) showed an overall increasing trend, but none passed the significance test (p > 0.01). Extremely strong precipitation (R99p) and Rx5day changed abruptly in 1994, and Rx1day and SDII changed abruptly in 2004. The seven extreme precipitation indices, except CDD and continuous wet days (CWD), had a 30-year cyclical pattern. The multi-year average of extreme precipitation indices showed that the CDD gradually decreased from north to south, CWD and R20mm gradually increased from north to south. Rx1day and Rx5day gradually increased from northwest to southeast, and SDII increased from west to east. The results can contribute valuable insights to extreme precipitation trends and future climate predictions in Henan Province and provide scientific support for coping with extreme precipitation changes and disaster prevention. Full article

(This article belongs to the Special Issue Observations and Modeling of Precipitation Extremes and Tropical Cyclones (2nd Edition))

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24 pages, 4625 KiB

Open AccessArticle

Extreme Temperature Index in China from a Statistical Perspective: Change Characteristics and Trend Analysis from 1961 to 2021

by Xulei Wang, Lifeng Wu and Huiying Liu

Atmosphere 2024, 15(11), 1398; https://doi.org/10.3390/atmos15111398 - 20 Nov 2024

Abstract

Against the backdrop of intensified global climate change, the frequency and intensity of extreme weather events in mainland China continue to rise due to its unique topography and complex climate types. In-depth research on the trends and impacts of climate extremes can help [...] Read more.

Against the backdrop of intensified global climate change, the frequency and intensity of extreme weather events in mainland China continue to rise due to its unique topography and complex climate types. In-depth research on the trends and impacts of climate extremes can help develop effective adaptation and mitigation strategies to protect the environment and enhance social resilience. In this research, temperature data from 2029 meteorological stations for the period 1961–2021 were used to study 15 extreme temperature indices and 3 extreme composite temperature indices. Linear propensity estimation and the Mann–Kendall test were applied to analyze the spatial and temporal variations in extreme temperatures in China, and Pearson’s correlation analysis was used to reveal the relationship between these indices and atmospheric circulation. The results show that in the past 60 years, the extreme temperature index in China has shown a trend of decreasing low-temperature events and increasing high-temperature events; in particular, the increase in warm nights is significantly higher than that of warm days. In terms of spatial distribution, daily maximum temperature less than the 10th percentile (TX10P) and daily minimum temperature greater than the 90th percentile (TN90P) increased significantly in the warm temperate sub-humid (WTSH) region, north subtropical humid (NSH) region, and marginal tropical humid (MTH) region, whereas frost days (FD0) and diurnal temperature range (DTR) decreased significantly. In the extreme composite temperature index, extreme temperature range (ETR) showed a downward trend, while compound heatwave (CHW) and compound heatwave and relative humidity (CHW-RH20) increased, with the latter mainly concentrated in the WTSH and NSH regions. Correlation analysis with climate oscillation shows that Arctic Oscillation (AO), Atlantic Multiannual Oscillation (AMO), and El Niño–Southern Oscillation (ENSO) are positively correlated with extremely high temperatures, whereas North Atlantic Oscillation (NAO) and Pacific Decadal Oscillation (PDO) are negatively correlated. Full article

(This article belongs to the Special Issue Climate Change and Regional Sustainability in Arid Lands)

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26 pages, 4939 KiB

Open AccessArticle

Daily Estimates of Global Radiation in the Brazilian Amazon from Simplified Models

by Charles Campoe Martim, Rhavel Salviano Dias Paulista, Daniela Castagna, Daniela Roberta Borella, Frederico Terra de Almeida, João Gabriel Ribeiro Damian and Adilson Pacheco de Souza

Atmosphere 2024, 15(11), 1397; https://doi.org/10.3390/atmos15111397 - 20 Nov 2024

Abstract

Solar radiation is an element and a meteorological factor that is present in several processes, such as evapotranspiration, photosynthesis, and energy generation, among others. However, in some regions, there is a limitation in surface data measurements. In this study, 87 empirical models were [...] Read more.

Solar radiation is an element and a meteorological factor that is present in several processes, such as evapotranspiration, photosynthesis, and energy generation, among others. However, in some regions, there is a limitation in surface data measurements. In this study, 87 empirical models were evaluated to estimate global radiation (Hg) in the Brazilian Amazon biome; these models were divided into five groups according to the input variables, such as insolation (group I), air temperature (group II), relative humidity (group III), astronomical variables (group IV), and hybrid models (group V). The estimates were evaluated by their significance (t-test) and then according to the statistical metrics of the models’ performance (R², MBE, RMSE, and d). The group V model

[H g / H_{0} = a + b l n ∆ T + c {(S / S_{0})}^{d}]

presented the best statistical performance in all the evaluated indicators, followed by the group I model

[H g / H_{0} = a + b {(S / S_{0})}^{c}]

, and then the group II model

[H g / H_{0} = a + b ∆ T + c {∆ T}^{0.25} + d {∆ T}^{0.5} + e (T m e d / H_{0})]

. The group III models presented a low statistical performance, and the group IV models were not significant (NS) in all the evaluated meteorological stations. In general, it was found that the simplified or hybrid models based on insolation and air temperature were efficient in estimating Hg in the Brazilian Amazon biome. Full article

(This article belongs to the Special Issue Solar Energy and Climate Change)

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18 pages, 4477 KiB

Open AccessArticle

Analysis of VLF Electromagnetic Scattering in Lower Anisotropic Ionosphere Based on Transfer Matrix

by Lin Zhao, Zhiting Zhan, Zhigang Zhang and Huiting Feng

Atmosphere 2024, 15(11), 1396; https://doi.org/10.3390/atmos15111396 - 19 Nov 2024

Abstract

Very-low-frequency (VLF) electromagnetic waves (3–30 kHz) are stable and attenuated, suitable for various applications in submarine communication and earthquake prediction. Very-low-frequency electromagnetic waves usually propagate in atmospheric waveguides formed between the anisotropic ionosphere at low to medium heights and the earth. However, the [...] Read more.

Very-low-frequency (VLF) electromagnetic waves (3–30 kHz) are stable and attenuated, suitable for various applications in submarine communication and earthquake prediction. Very-low-frequency electromagnetic waves usually propagate in atmospheric waveguides formed between the anisotropic ionosphere at low to medium heights and the earth. However, the electromagnetic parameters of the anisotropic ionosphere at low to medium heights are very complex, making it difficult to accurately calculate and analyze the scattering characteristics of very-low-frequency electromagnetic waves. This article divides the mid to low altitude anisotropic ionosphere into fine layers, and establishes a more accurate transmission model for ultra-low-frequency electromagnetic waves in the layered structure of ionization layers by deriving the anisotropy/transmission matrix of each layer. In the comparative verification, we calculated the field strength of 17 kHz VLF electromagnetic waves within a transmission distance range of 500–1600 km based on the proposed method and compared it with statistical data collected from actual communication experiments and theoretical calculation results based on traditional ITU-R P.372-11. The results show that compared with the theoretical results based on ITU-R P.372-11, the method proposed in this paper fully considers the vertical height non-uniformity of the ionosphere, and its calculated results are more consistent with actual measurement data, with higher accuracy. Our work provides excellent guidance for the development of precise models for the propagation and prediction of extremely low-frequency electromagnetic waves, as well as a good idea for the accurate calculation of VLF electromagnetic scattering within 500–1500 km. Full article

(This article belongs to the Special Issue Intelligent Modeling of the Ionosphere and Troposphere for Radio Application)

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19 pages, 6484 KiB

Open AccessArticle

Simulated Impacts of Thundercloud Charge Distributions on Sprite Halos Using a 3D Quasi-Electrostatic Field Model

by Jinbo Zhang, Jiawei Niu, Zhibin Xie, Yajun Wang, Xiaolong Li and Qilin Zhang

Atmosphere 2024, 15(11), 1395; https://doi.org/10.3390/atmos15111395 - 19 Nov 2024

Abstract

Sprite halos are transient luminous phenomena in the lower ionosphere triggered by tropospheric lightning. The effect of removed charge distributions on sprite halos has not been sufficiently discussed. A three-dimensional (3D) quasi-electrostatic (QES) field model was developed in this paper, including the ionospheric [...] Read more.

Sprite halos are transient luminous phenomena in the lower ionosphere triggered by tropospheric lightning. The effect of removed charge distributions on sprite halos has not been sufficiently discussed. A three-dimensional (3D) quasi-electrostatic (QES) field model was developed in this paper, including the ionospheric nonlinear effect and optical emissions. Simulation results show that, for a total charge of 150 C removed within 1 ms with different spatial distributions, higher altitudes of charge removal lead to stronger electric fields and increase sprite halos’ emission intensities. The non-axisymmetric horizontal distribution of charge affects mesospheric electric fields, and the corresponding scales and intensities of emissions vary with observation orientations. Considering the tilted dipole charge structure due to wind shear, the generated electric field and the corresponding position of sprite halos shift accordingly with the tropospheric removed charge, providing an explanation for the horizontal displacement between sprite halos and the parent lightning. Full article

(This article belongs to the Special Issue Impact of Thunderstorms on the Upper Atmosphere)

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20 pages, 717 KiB

Open AccessReview

Deep Learning-Based Atmospheric Visibility Detection

by Yawei Qu, Yuxin Fang, Shengxuan Ji, Cheng Yuan, Hao Wu, Shengbo Zhu, Haoran Qin and Fan Que

Atmosphere 2024, 15(11), 1394; https://doi.org/10.3390/atmos15111394 - 19 Nov 2024

Abstract

Atmospheric visibility is a crucial meteorological element impacting urban air pollution monitoring, public transportation, and military security. Traditional visibility detection methods, primarily manual and instrumental, have been costly and imprecise. With advancements in data science and computing, deep learning-based visibility detection technologies have [...] Read more.

Atmospheric visibility is a crucial meteorological element impacting urban air pollution monitoring, public transportation, and military security. Traditional visibility detection methods, primarily manual and instrumental, have been costly and imprecise. With advancements in data science and computing, deep learning-based visibility detection technologies have rapidly emerged as a research hotspot in atmospheric science. This paper systematically reviews the applications of various deep learning models—Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and Transformer networks—in visibility estimation, prediction, and enhancement. Each model’s characteristics and application methods are discussed, highlighting the efficiency of CNNs in spatial feature extraction, RNNs in temporal tracking, GANs in image restoration, and Transformers in capturing long-range dependencies. Furthermore, the paper addresses critical challenges in the field, including dataset quality, algorithm optimization, and practical application barriers, proposing future research directions, such as the development of large-scale, accurately labeled datasets, innovative learning strategies, and enhanced model interpretability. These findings highlight the potential of deep learning in enhancing atmospheric visibility detection techniques, providing valuable insights into the literature and contributing to advances in the field of meteorological observation and public safety. Full article

(This article belongs to the Special Issue Air Pollution Modeling and Observations in Asian Megacities)

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16 pages, 4556 KiB

Open AccessArticle

Evaluation of Radiation Rates and Health Hazards from Different Cement Types in Pakistan

by Muhammad Waseem, Hannan Younis, Moustafa Salouci, Mian Mateen Ullah, Muhammad Adil Khan, Ouazir Salem, Atef Abdelkader and Abd Haj Ismail

Atmosphere 2024, 15(11), 1393; https://doi.org/10.3390/atmos15111393 - 19 Nov 2024

Abstract

The raw materials of cement contain radioactive elements that come from natural sources. Members of the decay chains of uranium, thorium, and potassium radioisotope ⁴⁰K are the primary sources of this radioactivity. The natural radionuclide concentration levels in cement differ greatly depending [...] Read more.

The raw materials of cement contain radioactive elements that come from natural sources. Members of the decay chains of uranium, thorium, and potassium radioisotope ⁴⁰K are the primary sources of this radioactivity. The natural radionuclide concentration levels in cement differ greatly depending on different geographic areas. To estimate the radionuclides concentration in cement specimens from twelve diverse Pakistani companies, gamma-ray spectroscopy analysis was used in the study. ²²⁶Ra, ²³²Th, and ⁴⁰K had activity concentration levels ranging from 18.08 to 43.18 Bq/kg, 16.73 to 23.53 Bq/kg, and 14.24 to 315.22 Bq/kg, respectively. The United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR) threshold for the ²²⁶Ra was surpassed by five of the studied samples. The indoor and outdoor dose rates as well as different radiological health hazard indices were also examined. The Indoor Absorbed Dosage (D_in) for some of the samples exceeded the permissible limit. These samples also had a high Indoor Effective Lifetime Cancer Risk (ELCR) factor, which makes them unsafe for interior construction purposes. The outdoor dosages as well as the hazard indices were well within the permitted ranges. The outdoor ELCR factor is low for all the cement brands, which makes them safe for exterior construction purposes. The findings were compared with published data from other countries around the globe. Finally, a thorough statistical analysis was performed and Pearson’s Correlation Coefficient (r) exhibited a very strong correlation between the different outdoor and indoor radiological health hazard indices. Full article

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

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15 pages, 4565 KiB

Open AccessArticle

Weather Regimes in Northern Eurasia: Statistics, Predictability and Associated Weather Anomalies

by Boris A. Babanov and Vladimir A. Semenov

Atmosphere 2024, 15(11), 1392; https://doi.org/10.3390/atmos15111392 - 19 Nov 2024

Abstract

A weather regimes approach is applied to study large-scale circulation patterns over the North Eurasian sector (NE, defined as 0–180° E and 40–80° N domain) during the 1940–2022 period using ERA5 reanalysis data. We identified four NE weather regimes (WRs) both for boreal [...] Read more.

A weather regimes approach is applied to study large-scale circulation patterns over the North Eurasian sector (NE, defined as 0–180° E and 40–80° N domain) during the 1940–2022 period using ERA5 reanalysis data. We identified four NE weather regimes (WRs) both for boreal winter and summer seasons using a k-means standard cluster analysis method for daily geopotential height fields (z500). Whereas the winter NE WRs do not have any significant long-term changes in occurrence, three summer NE WRs are found to have statistically significant linear trends of occurrence over the studied period, the most notable of which is a regime associated with anticyclonic activity centered over the Ural Mountains called Summer Ural High, which has a statistically significant trend of +2.4 days of seasonal occurrence per decade. Loops of statistically significant likely transitions are found for the NE WRs in both seasons, showing that the NE WRs evolution follows preferred paths and is not purely random. NE WRs’ seasonal occurrence is found to change depending on the phase of the El Nino oscillation and Northern Hemisphere sea-ice area anomalies in preceding seasons. El Nino events in autumn are associated with an increased occurrence of the Winter Scandinavian Blocking regime and a decreased occurrence of the Winter North Eastern High regime. Negative sea-ice area anomalies in autumn are associated with an increased occurrence of the Winter Ural High and a decreased occurrence of the Winter Scandinavian Low regime. NE WR’s are associated with considerable, up to several times, changes in probability of temperature and precipitation extremes over the NE region. Full article

(This article belongs to the Section Climatology)

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18 pages, 13617 KiB

Open AccessArticle

Observation and Numerical Simulation of Cross-Mountain Airflow at the Hong Kong International Airport from Range Height Indicator Scans of Radar and LIDAR

by Ying Wa Chan, Kai Wai Lo, Ping Cheung, Pak Wai Chan and Kai Kwong Lai

Atmosphere 2024, 15(11), 1391; https://doi.org/10.3390/atmos15111391 - 19 Nov 2024

Abstract

Apart from headwind changes, crosswind changes may be hazardous to aircraft operation. This paper presents two cases of recently observed crosswind changes from the range height indicator scans of ground-based remote sensing meteorological equipment, namely an X-band microwave radar and a short-range LIDAR. [...] Read more.

Apart from headwind changes, crosswind changes may be hazardous to aircraft operation. This paper presents two cases of recently observed crosswind changes from the range height indicator scans of ground-based remote sensing meteorological equipment, namely an X-band microwave radar and a short-range LIDAR. Both instruments have a range resolution down to around 30 m, allowing the study of fine-scale details of the vertical profiles of cross-mountain airflow at the Hong Kong International Airport. Rapidly evolving winds have been observed by the equipment in tropical cyclone situations, revealing high levels of turbulence and vertically propagating waves. The eddy dissipation rate derived from radar spectrum width indicated severe turbulence, with values exceeding 0.5 m^2/3 s⁻¹. In order to study the feasibility of predicting such disturbed airflow, a mesoscale meteorological model and a computational fluid dynamics model with high spatial resolution are used in this paper. It is found that the mesoscale meteorological model alone is sufficient to capture some rapidly evolving airflow features, including the turbulence level, the waves, and the rapidly changing wind speeds. However, the presence of reverse flow could only be reproduced with the use of a building-resolving computational fluid dynamics model. This paper aims at providing a reference for airports to consider the feasibility of performing high-resolution numerical simulations of rapidly evolving airflow to alert the pilots in advance for airports in complex terrains and the setup of buildings. Full article

(This article belongs to the Special Issue Tropical Cyclones: Observations and Prediction (2nd Edition))

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23 pages, 15800 KiB

Open AccessArticle

A Reanalysis Precipitation Integration Method Utilizing the Generalized Three-Cornered Hat Approach and High-Resolution, Gauge-Based Datasets

by Lilan Zhang, Xiaohong Chen, Bensheng Huang, Jie Liu, Daoyi Chen, Liangxiong Chen, Rouyi Lai and Yanhui Zheng

Atmosphere 2024, 15(11), 1390; https://doi.org/10.3390/atmos15111390 - 18 Nov 2024

Abstract

The development of high-precision, long-term, hourly-scale precipitation data is essential for understanding extreme precipitation events. Reanalysis systems are particularly promising for this type of research due to their long-term observations and wide spatial coverage. This study aims to construct a more robust precipitation [...] Read more.

The development of high-precision, long-term, hourly-scale precipitation data is essential for understanding extreme precipitation events. Reanalysis systems are particularly promising for this type of research due to their long-term observations and wide spatial coverage. This study aims to construct a more robust precipitation dataset by integrating three widely-used reanalysis precipitation estimates: Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA2), Climate Forecast System Reanalysis (CFSR), and European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5). A novel integration method based on the generalized three-cornered hat (TCH) approach is employed to quantify uncertainties in these products. To enhance accuracy, the high-density daily precipitation data from the Asian Precipitation-Highly-Resolved Observation Data Integration Towards Evaluation (APHRODITE) dataset is used for correction. Results show that the TCH method effectively identifies seasonal and spatial uncertainties across the products. The TCH-weighted product (TW), calculated using signal-to-noise ratio weighting, outperforms the original reanalysis datasets across various watersheds and seasons. After correction with APHRODITE data, the enhanced integrated product (ATW) significantly improves accuracy, making it more suitable for extreme precipitation event analysis. Quantile mapping was applied to assess the ability of TW and ATW to represent extreme precipitation. Both products showed improved accuracy in regional average precipitation, with ATW demonstrating superior improvement. This integration method provides a robust approach for refining reanalysis precipitation datasets, contributing to more reliable hydrological and climate studies. Full article

(This article belongs to the Special Issue Advances in Rainfall-Induced Hazard Research)

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14 pages, 1157 KiB

Open AccessArticle

Concrete Actions for Improving Indoor Air Quality in Korea: Advancing Public Health for Vulnerable Populations

by Seongho Jeon, Hyosun Lee, Kyunghee Jo, Yongsung Park, Wonsuck Yoon and Jaewook Choi

Atmosphere 2024, 15(11), 1389; https://doi.org/10.3390/atmos15111389 - 18 Nov 2024

Abstract

In modern society, individuals spend an increasing amount of time indoors, emphasizing the importance of understanding the health impacts of indoor environments. This study focused on measuring indoor air quality to identify vulnerable populations and observe the effects of residential environment improvements on [...] Read more.

In modern society, individuals spend an increasing amount of time indoors, emphasizing the importance of understanding the health impacts of indoor environments. This study focused on measuring indoor air quality to identify vulnerable populations and observe the effects of residential environment improvements on air quality. Targeting low-income families and elderly households, known for their heightened vulnerability to environmental health risks, the study involved direct visits to 2328 low-income households across 16 cities and provinces in South Korea from 2021 to 2022. Indoor air quality parameters, including PM_2.5, PM₁₀, total volatile organic compounds (TVOC), formaldehyde (HCHO), and airborne mold, were measured. Among these households, 300 with critically compromised living conditions received support for wallpaper and paneling replacement. Comparative measurements before and after the renovations revealed that single-person households had higher levels of PM_2.5 and TVOC compared with households with four or more members. Additionally, households with elevated concentrations of airborne mold also exhibited higher levels of PM_2.5 and PM₁₀. Importantly, households that received environmental improvements showed a significant reduction in airborne mold concentration by approximately 50% or more. This study underscores the importance of indoor environmental health and provides valuable evidence supporting policies focused on health promotion and residential welfare improvements for vulnerable populations. The research is distinguished by its comprehensive nature, involving direct measurements from nearly 2000 households nationwide, rather than relying solely on secondary data. Full article

(This article belongs to the Topic Impacts of Air Quality on Environment and Human Health)

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