Atmosphere

14 pages, 1014 KiB

Open AccessArticle

The Spatiotemporal Variation Trends of Major Air Pollutants in Beijing from 2014 to 2023

by Yangyang Xie and Jiaqing Zhao

Atmosphere 2025, 16(5), 494; https://doi.org/10.3390/atmos16050494 - 24 Apr 2025

Based on the hourly concentration data of PM₂.₅, PM₁₀, SO₂, NO₂, CO, and O₃ from 35 environmental monitoring sites in Beijing between 1 January 2014 and 31 December 2023, this paper investigated [...] Read more.

Based on the hourly concentration data of PM₂.₅, PM₁₀, SO₂, NO₂, CO, and O₃ from 35 environmental monitoring sites in Beijing between 1 January 2014 and 31 December 2023, this paper investigated the annual average concentration variation of these pollutants, the differences between regions, and the factors influencing these changes and differences. Seasonal variations in the pollutants are examined through monthly average concentrations, and Pearson correlation coefficients are used to study their relationships. The results are as follows: (1) Over the past decade, the concentrations of PM₂.₅, PM₁₀, SO₂, NO₂, and CO have decreased by −67.5%, −58.6%, −81.4%, −51.9%, and −59.3%, respectively, indicating significant progress in controlling these pollutants. However, O₃ fluctuates significantly between 57 μg/m³ and 66 μg/m³, suggesting the need to improve O₃ management. (2) Air pollution levels exhibit distinct spatial variations, with better air quality in mountainous and suburban areas compared to more heavily trafficked urban zones, emphasizing the need for localized control strategies. (3) The correlation coefficients between PM₂.₅, PM₁₀, SO₂, NO₂, and CO all exceeded 0.90, indicating strong positive correlations. In contrast, O₃ showed negative correlations with these five pollutants, with its most pronounced negative correlation being NO₂. Full article

(This article belongs to the Section Air Quality)

24 pages, 1330 KiB

Open AccessArticle

Analysis of Spatio-Temporal Variation Characteristics of Air Pollutants in Zaozhuang China from 2018 to 2022

by Xiaoli Xia and Shangpeng Sun

Atmosphere 2025, 16(5), 493; https://doi.org/10.3390/atmos16050493 - 24 Apr 2025

Abstract

Based on the air-quality monitoring data of Zaozhuang City from 2018 to 2022, this study systematically analyzed the spatio-temporal variation characteristics of multiple pollutants by comprehensively applying Kriging interpolation, time-series decomposition, wavelet transform, and DBSCAN spatial clustering methods. The key findings include: (1) [...] Read more.

Based on the air-quality monitoring data of Zaozhuang City from 2018 to 2022, this study systematically analyzed the spatio-temporal variation characteristics of multiple pollutants by comprehensively applying Kriging interpolation, time-series decomposition, wavelet transform, and DBSCAN spatial clustering methods. The key findings include: (1) Overall, air pollutant concentrations in Zaozhuang decrease from 2018 to 2022, with NO₂, SO₂, PM_2.5, and PM₁₀ concentrations declining by 17.3%, 52.2%, 28.9%, and 33.6%, respectively. However, O₃ concentration increases by 2.5% in 2022 compared to 2018. Seasonally, SO₂, PM_2.5, and PM₁₀concentrations are the highest in winter and lowest in summer, while CO, NO₂, and O₃ follow a winter > autumn > spring > summer pattern. Weekly variations show that daily average concentrations of CO, NO₂, SO₂, PM_2.5, and PM₁₀ peak on Mondays, with concentrations slightly higher on weekdays than weekends. (2) Spatially, CO, NO₂, PM_2.5, and PM₁₀ concentrations are higher in the southern region, while O₃ and SO₂concentrations are elevated in Shizhong District, Xuecheng District, and Tengzhou City. (3) Correlation analysis reveals that meteorological parameters, such as precipitation, significantly influence pollutant concentrations, with precipitation playing a role in reducing pollutant levels. This study highlights the effectiveness of the Kriging method in analyzing the complex spatio-temporal dynamics of air pollutants, offering valuable insights for environmental policy and urban planning. Full article

(This article belongs to the Section Meteorology)

12 pages, 1063 KiB

Open AccessArticle

An Improved One-Dimensional Variational Method for a Ground-Based Microwave Radiometer

by Hualong Yan, Di Zhou, Renxin Ji and Rongmei Geng

Atmosphere 2025, 16(5), 492; https://doi.org/10.3390/atmos16050492 - 24 Apr 2025

Abstract

Temperature and water vapor density profiles in the troposphere (from the surface to 10 km) can be retrieved from a ground-based microwave radiometer (MWR) at high temporal and moderate vertical resolution. The back-propagation neural network (BPNN) algorithm is commonly deployed in ground-based microwave [...] Read more.

Temperature and water vapor density profiles in the troposphere (from the surface to 10 km) can be retrieved from a ground-based microwave radiometer (MWR) at high temporal and moderate vertical resolution. The back-propagation neural network (BPNN) algorithm is commonly deployed in ground-based microwave radiometers. Some studies have shown that the accuracy of the BPNN retrieval algorithm is affected by training data with large deviations. In this paper, an improved 1D-VAR method is proposed, which can effectively correct the bias; the results show that the improved 1D-VAR method can provide more accurate inversion results. Compared to the BPNN and 1D-VAR methods, the root mean square errors of temperature for the improved 1D-VAR method are reduced by

60.8 %

and

29.4 %

during daytime and by

54.2 %

and

49.7 %

during nighttime, respectively. Full article

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

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19 pages, 7514 KiB

Open AccessArticle

Exploring the Influencing Factors of Surface Ozone Variability by Explainable Machine Learning: A Case Study in the Basilicata Region (Southern Italy)

by Roberta Valentina Gagliardi and Claudio Andenna

Atmosphere 2025, 16(5), 491; https://doi.org/10.3390/atmos16050491 - 24 Apr 2025

Abstract

Exposure to high surface ozone (O₃) concentrations, which is a major air pollutant and greenhouse gas, constitutes a significant public health concern, especially considering the potential adverse impact of climate change on future O₃ values. The implementation of increasingly effective [...] Read more.

Exposure to high surface ozone (O₃) concentrations, which is a major air pollutant and greenhouse gas, constitutes a significant public health concern, especially considering the potential adverse impact of climate change on future O₃ values. The implementation of increasingly effective methods to assess the factors determining the formation and variability of O₃ is, therefore, of great significance. In this study, a methodological approach combining both supervised and unsupervised machine learning algorithms (MLAs) with the Shapley additive explanations (SHAP) method was used to understand the key factors behind O₃ variability and to explore the nonlinear relationships linking O₃ to these factors. The SHAP analysis carried out at different event scales indicated (i) the dominant role of the meteorological variables in driving O₃ variability, mainly relative humidity, wind speed, and temperature throughout the study period; (ii) an increase in the contribution of temperature, nitrogen oxides, and carbon monoxide to high O₃ concentrations during a selected pollution event; (iii) the predominant effect of wind speed and relative humidity in shaping the O₃ daily patterns clustered using the k-means technique. The results obtained are expected to be useful for the definition of effective measures to prevent and/or mitigate the health damage associated with ozone exposure. Full article

(This article belongs to the Section Air Quality)

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27 pages, 2796 KiB

Open AccessArticle

The Applications of AI Tools in the Fields of Weather and Climate—Selected Examples

by Agnieszka Krzyżewska

Atmosphere 2025, 16(5), 490; https://doi.org/10.3390/atmos16050490 - 23 Apr 2025

Abstract

Large language models (LLMs) based on artificial intelligence have found applications across various sectors—including medicine, education, science, literature, and marketing. Although they offer considerable opportunities, their limitations also raise important concerns. This study evaluates several AI tools in the context of meteorology and [...] Read more.

Large language models (LLMs) based on artificial intelligence have found applications across various sectors—including medicine, education, science, literature, and marketing. Although they offer considerable opportunities, their limitations also raise important concerns. This study evaluates several AI tools in the context of meteorology and climatology. The tools examined include ChatGPT o3-mini, o1, 4.o, 4.0; Gemini Advanced 1.5 and 2.0; Copilot; Perplexity; DataAnalyst; Consensus; ScholarGPT; SciSpace; Claude; and DeepSeek. The evaluation tasks comprised cloud recognition and classification from photographs, gap-filling in literature reviews, map creation based on provided datasets, comparative interpretation of maps, and archival data retrieval from line graphs converted to numerical data. Each task was rated on a 0–5 scale. Conducted between February 2024 and February 2025, the study found that ChatGPT o3-mini excelled in cloud classification; ChatGPT4.o and ScholarGPT produced high-quality maps; Claude 3.5 Sonnet and SciSpace provided the most detailed map descriptions; and Consensus and ChatGPT o1 were the most effective for literature review support. However, all tools performed poorly in regards to archival data retrieval, with Claude 3.5 Sonnet yielding the smallest errors. Overall, substantial progress was observed over the study period. Full article

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

19 pages, 3144 KiB

Open AccessArticle

Short-Term Temporal Variability of Radon in Finnish Dwellings and the Use of Temporal Correction Factors

by Tuukka Turtiainen, Katja Kojo and Päivi Kurttio

Atmosphere 2025, 16(5), 489; https://doi.org/10.3390/atmos16050489 - 23 Apr 2025

Abstract

(1) Background: Affordable electronic radon instruments have become increasingly popular as alternatives to traditional home radon measurements, which require a minimum duration of two months. This study aimed to determine how results obtained from these devices should be interpreted and whether short-term measurements [...] Read more.

(1) Background: Affordable electronic radon instruments have become increasingly popular as alternatives to traditional home radon measurements, which require a minimum duration of two months. This study aimed to determine how results obtained from these devices should be interpreted and whether short-term measurements lasting 2–5 days can be reliably used to assess the need for radon remediation in buildings, estimate residents’ exposure, or assess public exposure. (2) Methods: A year-long radon measurement was conducted in 55 dwellings, selected to represent the Finnish housing stock as accurately as possible. Radon concentrations were recorded hourly, and the results were analysed using probabilistic analysis to calculate the likelihood of erroneous assessments. (3) Results: If a maximum false-negative rate of 1% is accepted, the action level for a 2–5-day measurement is 90–100 Bq/m³. For measurements exceeding this threshold, a longer measurement period is necessary. (4) Conclusions: Based on this study, short-term radon measurements cannot yet be recommended as a replacement for current methods. However, the study revealed significant radon level fluctuations in September, suggesting that this period should be reconsidered for inclusion in the measurement season. Full article

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

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15 pages, 4501 KiB

Open AccessArticle

External Vibration-Assisted Carbon Dioxide Sequestration in Heavy Oil Reservoirs: The Influences of Frequency and Cavity Distribution

by Shixuan Lu, Zhengyuan Zhang, Liming Dai and Na Jia

Atmosphere 2025, 16(5), 488; https://doi.org/10.3390/atmos16050488 - 22 Apr 2025

Abstract

This study investigates the effect of external vibration stimulation on CO₂ dissolution behavior in heavy oil reservoirs, focusing on the influence of vibration frequency and cavity distribution within porous media. Experiments reveal that 5 Hz vibration significantly enhances CO₂ dissolution, while [...] Read more.

This study investigates the effect of external vibration stimulation on CO₂ dissolution behavior in heavy oil reservoirs, focusing on the influence of vibration frequency and cavity distribution within porous media. Experiments reveal that 5 Hz vibration significantly enhances CO₂ dissolution, while higher frequencies (10 Hz and 20 Hz) hinder the process. A more homogeneous and extensive distribution of oil-depleted cavities further improves dissolution rates, particularly in post-gas flooding scenarios. The dissolution process, observed under constant pressure conditions, is categorized into three stages: cavity filling, fast dissolution, and slow dissolution. Vibration stimulation effectively enhances the fast dissolution stage but has a minimal impact on the slow dissolution stage. Intermittent vibration shows mixed effects, improving dissolution at 100% oil saturation but reducing rates at 90% saturation due to cavity-induced flow disruptions. These findings demonstrate the potential of vibration-stimulated CO₂ dissolution (VS-CO₂ dissolution) as a novel technique for enhancing CO₂ storage and heavy oil recovery in reservoirs. This study provides critical insights for optimizing vibration frequency and cavity distribution, paving the way for improved field applications of this innovative technology. Full article

(This article belongs to the Special Issue CO₂ Geological Storage and Utilization (2nd Edition))

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32 pages, 13922 KiB

Open AccessArticle

Urban Air Pollution in the Global South: A Never-Ending Crisis?

by Rasa Zalakeviciute, Jesus Lopez-Villada, Alejandra Ochoa, Valentina Moreno, Ariana Byun, Esteban Proaño, Danilo Mejía, Santiago Bonilla-Bedoya, Yves Rybarczyk and Fidel Vallejo

Atmosphere 2025, 16(5), 487; https://doi.org/10.3390/atmos16050487 - 22 Apr 2025

Abstract

Among the challenges the human population needs to address are threats of global pandemics, increasing socioeconomic inequality, especially in developing countries, and anthropogenic climate change. The latter’s effect has been amplified with the arrival of 2023/24 El Niño, causing an exceptional drought in [...] Read more.

Among the challenges the human population needs to address are threats of global pandemics, increasing socioeconomic inequality, especially in developing countries, and anthropogenic climate change. The latter’s effect has been amplified with the arrival of 2023/24 El Niño, causing an exceptional drought in the Amazon basin, significantly affecting fire conditions and hydroelectric power production in several South American countries, including Ecuador. This study analyzes five criteria pollutants—carbon monoxide (CO), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone (O₃), and particulate matter ≤ 2.5 µm (PM_2.5)—during 2019–2024 in Quito, Ecuador, a high-elevation tropical metropolis. Despite long-term efforts to regulate emissions, air pollution levels continue to rise, driven by overlapping crises, including energy shortages, political unrest, and extreme weather events. The persistent failure to improve air quality underscores the vulnerability of developing nations to climate change-induced energy instability and the urgent need for adaptive, diversified, and resilient future energy planning. Without immediate shifts in climate adaptation policies, cities like Quito will continue to experience worsening air quality, with severe implications for public health and environmental sustainability. Full article

(This article belongs to the Special Issue Air Quality in Metropolitan Areas and Megacities (Second Edition))

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13 pages, 10859 KiB

Open AccessArticle

A Lightning Very-High-Frequency Mapping DOA Method Based on L Array and 2D-MUSIC

by Chuansheng Wang, Nianwen Xiang, Zhaokun Li, Zengwei Lyu, Yu Yang and Huaifei Chen

Atmosphere 2025, 16(5), 486; https://doi.org/10.3390/atmos16050486 - 22 Apr 2025

Abstract

Lightning Very-High-Frequency (VHF) radiation source mapping technology represents a pivotal advancement in the study of lightning discharge processes and their underlying physical mechanisms. This paper introduces a novel methodology for reconstructing lightning discharge channels by employing the Multiple Signal Classification (MUSIC) algorithm to [...] Read more.

Lightning Very-High-Frequency (VHF) radiation source mapping technology represents a pivotal advancement in the study of lightning discharge processes and their underlying physical mechanisms. This paper introduces a novel methodology for reconstructing lightning discharge channels by employing the Multiple Signal Classification (MUSIC) algorithm to estimate the Direction of Arrival (DOA) of lightning VHF radiation sources, specifically tailored for both non-uniform and uniform L-shaped arrays (2D-MUSIC). The proposed approach integrates the Random Sample Consensus (RANSAC) algorithm with 2D-MUSIC, thereby enhancing the precision and robustness of the reconstruction process. Initially, the array data are subjected to denoising via the Ensemble Empirical Mode Decomposition (EEMD) algorithm. Following this, the covariance matrix of the processed array data is decomposed to isolate the signal subspace, which corresponds to the signal components, and the noise subspace, which is orthogonal to the signal components. By exploiting the orthogonality between these subspaces, the method achieves an accurate estimation of the signal incidence direction, thereby facilitating the precise reconstruction of the lightning channel. To validate the feasibility of this method, comprehensive numerical simulations were conducted, revealing remarkable accuracy with elevation and azimuth angle errors both maintained below 1 degree. Furthermore, VHF non-uniform and uniform L-shaped lightning observation systems were established and deployed to analyze real lightning events occurring in 2021 and 2023. The empirical results demonstrate that the proposed method effectively reconstructs lightning channel structures across diverse L-shaped array configurations. This innovative approach significantly augments the capabilities of various broadband VHF arrays in radiation source imaging and makes a substantial contribution to the study of lightning development processes. The findings of this study underscore the potential of the proposed methodology to advance our understanding of lightning dynamics and enhance the accuracy of lightning channel reconstruction. Full article

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

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Atmosphere, Volume 16, Issue 5 (May 2025) – 9 articles

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