Atmosphere

23 pages, 4659 KB

Open AccessArticle

The Impact of COVID-19 on Civil Aviation Emissions: A High-Resolution Inventory Study in Eastern China’s Industrial Province

by Chuanyong Zhu, Baodong Jiang, Mengyi Qiu, Na Yang, Lei Sun, Chen Wang, Baolin Wang, Guihuan Yan and Chongqing Xu

Atmosphere 2025, 16(8), 994; https://doi.org/10.3390/atmos16080994 - 21 Aug 2025

Viewed by 514

Abstract

Emissions from civil aviation not only degrade the environmental quality around airports but also have the significant effects on climate change. According to the flight schedules, aircraft/engine combination information and revised emission factors from the International Civil Aviation Organization (ICAO) Aircraft Engine Emission [...] Read more.

Emissions from civil aviation not only degrade the environmental quality around airports but also have the significant effects on climate change. According to the flight schedules, aircraft/engine combination information and revised emission factors from the International Civil Aviation Organization (ICAO) Aircraft Engine Emission Databank (EEDB) based on meteorological data, the emissions of climate forcers (CFs: BC, CH₄, CO₂, H₂O, and N₂O), conventional air pollutants (CAPs: CO, HC, NO_X, OC, PM_2.5, and SO₂), and hazardous heavy metals (HMs: As, Cu, Ni, Se, Cr, Cd, Hg, Pb, and Zn) from flights of civil aviation of eight airports in Shandong in 2018 and 2020 are estimated in this study. Moreover, the study quantifies the impact of COVID-19 on civil aviation emissions (CFs, CAPs, and HMs) in Shandong, revealing reductions of 47.45%, 48.03%, and 47.45% in 2020 compared to 2018 due to flight cuts. By 2020, total emissions reach 9075.44 kt (CFs), 35.57 kt (CAPs), and 0.51 t (HMs), with top contributors being Qingdao Liuting International Airport (ZSQD) (39.60–40.37%), Shandong Airlines (26.56–28.92%), and B738 aircraft (42.98–46.70%). As byproducts of incomplete fuel combustion, the shares of CO (52.40%) and HC (47.76%) emissions during taxi/ground idle mode are significant. In contrast, emissions during cruise phase are the dominant contributor of other species with a share of 74.67–95.61% of the associated total emissions. The findings highlight the disproportionate role of specific airlines, aircraft, and operational phases in regional aviation pollution. By bridging gaps in localized emission inventories and flight-phase analyses, this research supports targeted mitigation strategies, such as fleet modernization and ground operation optimization, to improve air quality in Shandong. The study highlights how sudden shifts in demand, such as those caused by pandemics, can significantly alter emission profiles, providing insights for sustainable aviation planning. Full article

(This article belongs to the Special Issue Aviation Emissions and Their Impact on Air Quality)

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19 pages, 5122 KB

Open AccessReview

An Overview of the Holocene High Sea Level Around the South China Sea: Age, Height, and Mechanisms

by Lei Zhang, Tongyan Lü, Lei Xue, Weiming Mo, Chaoqun Wang, Xitao Zhao and Daogong Hu

Atmosphere 2025, 16(8), 993; https://doi.org/10.3390/atmos16080993 - 21 Aug 2025

Viewed by 999

Abstract

Understanding Holocene high sea levels in the South China Sea (SCS) is critical for understanding climate change and assessing future sea-level rise risks. We provide a comprehensive review of the Holocene highstand in the SCS, focusing on its age, height, and mechanisms. Records [...] Read more.

Understanding Holocene high sea levels in the South China Sea (SCS) is critical for understanding climate change and assessing future sea-level rise risks. We provide a comprehensive review of the Holocene highstand in the SCS, focusing on its age, height, and mechanisms. Records reveal a wide range for this highstand: ages span 3480–7500 cal yr BP, while elevations range from −7.40 to 7.53 m relative to the present. Positive elevations dominate (80.5% of records), with the most frequent range being 2–3 m. Regionally averaged formation times suggest a broadly synchronous mid-Holocene high-sea-level event across the SCS, potentially reflecting a global background. The observed variability is attributed to the interplay of multiple factors: global processes like glacial meltwater input and seawater thermal expansion, particularly during the Holocene warm period, and regional neotectonic movements (uplift/subsidence), which are the primary cause of spatial differences in reconstructed elevations. Significant debate persists regarding precise timing, height, and dominant mechanisms due to limitations in data coverage, dating precision, and challenges in quantifying tectonic influences. Future research priorities include obtaining high-resolution data from stable marine sediments, employing diverse dating techniques and modern crustal deformation monitoring, quantifying tectonic impacts, developing regional sea-level models, and enhancing international collaboration to refine understanding and improve predictions of future sea-level rise impacts. Full article

(This article belongs to the Special Issue The Evolution of Climate and Environment in the Holocene)

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27 pages, 1502 KB

Open AccessReview

Monitoring of Air Pollution from the Iron and Steel Industry: A Global Bibliometric Review

by Ekaterina Zolotova, Natalya Ivanova and Sezgin Ayan

Atmosphere 2025, 16(8), 992; https://doi.org/10.3390/atmos16080992 - 21 Aug 2025

Viewed by 766

Abstract

The iron and steel industry is one of the main industrial contributors to air pollution. The aim of our study is to analyze modern studies on air pollution by the iron and steel industry, as a result of which the geography and research [...] Read more.

The iron and steel industry is one of the main industrial contributors to air pollution. The aim of our study is to analyze modern studies on air pollution by the iron and steel industry, as a result of which the geography and research directions and the degree of development of current issues will be assessed, and the most cited articles and journals will be identified. A review of contemporary research (2018–2024) was conducted on the basis of articles with a digital object identifier (DOI) using machine learning methodologies (VOSviewer software version 1.6.20). The number of articles selected was 80. The heat map of study density clearly showed that the geographic distribution of studies was extremely uneven. A total of 65% of the studies were conducted in China, 9% in Nigeria, 6% in Russia, 3% in Poland, and 3% in Turkey. The remaining 14% of articles represent a series of single studies conducted in 11 countries. The revealed geographical imbalance between countries with developed production and the number of studies conducted in them shows a significant shortcoming in monitoring research. Most of the studies (20%) were devoted to the assessment of multicomponent emissions. A special place among them was occupied by the inventory of emissions using various methods. The next main directions in terms of the number of articles were aimed at studying the toxic metal emissions (19%), at the analysis of organic emissions (19%), at the modeling and forecasting of emissions (18%), and at particulate matter studies (15%). The main features of the articles for each direction are briefly noted. Citation analysis made it possible to compile a rating of articles of greatest scientific interest and the most authoritative journals. Citation network analysis revealed important insights into the structure of scientific communication in the monitoring of atmospheric pollution from the iron and steel industry. The results of our review will contribute to the consolidation of scientists, the identification of gaps in scientific knowledge, and the improvement of environmental policy and technological solutions. Full article

(This article belongs to the Section Air Pollution Control)

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18 pages, 7230 KB

Open AccessArticle

Improving Urban Air Quality: Evaluation of Electric Vehicles and Nature-Based Solutions as Source and Sink Abatement Strategies for Ozone Pollution in Toronto, ON, Canada

by William A. Gough, Vidya Anderson and Matej Zgela

Atmosphere 2025, 16(8), 991; https://doi.org/10.3390/atmos16080991 - 21 Aug 2025

Viewed by 640

Abstract

In this study, two air pollution abatement strategies are examined, focusing on sources and sinks. These include the reduction in ozone precursors (source) and impact of nature-based solutions (sink). For the first abatement strategy (source), two waves of COVID-19 lockdown periods are leveraged [...] Read more.

In this study, two air pollution abatement strategies are examined, focusing on sources and sinks. These include the reduction in ozone precursors (source) and impact of nature-based solutions (sink). For the first abatement strategy (source), two waves of COVID-19 lockdown periods are leveraged as proxies for the potential abatement of air quality pollutants in Toronto, Ontario, Canada, that could occur through electric vehicle deployment. Ground level ozone (O₃) and its precursors (NO, NO₂), were examined from April to December 2020, during the first two pandemic lockdown periods in Toronto. An ozone weekend effect framework was used to evaluate changes. Results showed that ozone precursors were the lowest of any of the preceding 10 years for both weekdays and weekends; however, ozone concentrations did not have a corresponding decrease but rather had a marked increase for both weekdays and weekends. These findings reflect reduced vehicular traffic and the ozone chemistry in an NOx-saturated (VOC-limited) environment. For the second abatement strategy (sink), a comparison of surface NO₂ observations and NO₂ satellite data showed the benefits of nature-based solutions as a sink abatement strategy, with the 2020 reduction amplified at the surface. Given the lack of ozone abatement realized through source reduction, deployment of nature-based solutions as a pollutant sink may present a more effective strategy for ground-level ozone abatement. Full article

(This article belongs to the Special Issue Nature-Based Countermeasures in Atmospheric and Climate Research)

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34 pages, 3632 KB

Open AccessReview

Systematic Review and Meta-Analysis of Urban Air Quality in the Arabian Peninsula

by Elisephane Irankunda, Monica Menendez, Basit Khan, Francesco Paparella and Olivier Pauluis

Atmosphere 2025, 16(8), 990; https://doi.org/10.3390/atmos16080990 - 20 Aug 2025

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Abstract

Air pollution is causing a global health, climate, and environmental crisis. Air quality (AQ) in hyper-arid regions, such as the Arabian Peninsula, remains under-explored, posing significant concerns for public health and the scientific community. Both long-term and short-term exposure to high pollutant levels, [...] Read more.

Air pollution is causing a global health, climate, and environmental crisis. Air quality (AQ) in hyper-arid regions, such as the Arabian Peninsula, remains under-explored, posing significant concerns for public health and the scientific community. Both long-term and short-term exposure to high pollutant levels, whether from anthropogenic or natural sources, can pose serious health risks. This paper offers a comprehensive review and meta-analysis of urban AQ literature published in the region over the past decade (2013–June 2025). We aim to provide guidance and highlight key directions for future research in the field. This paper examines key pollutants, emission sources, implications of urban sources, and the most studied countries, methodologies, limitations, and recommendations from different case studies. Our analysis reveals a significant research gap highlighting insufficient recent literature. Saudi Arabia was the most studied country with 20 papers, followed by the broader Arabian Peninsula (sixteen), Qatar (twelve), the United Arab Emirates and Iraq (seven each), Kuwait (four), Oman (three), Jordan, and Bahrain (one each). The primary methods employed included measurements and sampling (28%) and remote sensing (24%), with a focus on pollutants such as dust (23.1%), NO_x/NO₂/NO (17.2%), PM_2.5 (17.6%), and PM₁₀ (12%). Industrial emissions (27%) and natural dust (24%) were identified as significant emission sources. Monitoring methods included grab sampling (19%), integrated sampling (34%), and continuous monitoring (47%). Notably, 13.3% of AQ sensors were linked to a station, 27.6% were self-referenced, and 59.1% did not specify calibration methods. The findings highlight the need for further research, regular calibration of air quality monitors, and the integration of advanced modeling approaches. Moreover, we recommend exploring the links between air pollution and urban development to ensure cleaner air and contribute to the global dialogue on sustainable and cross-border AQ solutions. Full article

(This article belongs to the Section Air Quality)

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16 pages, 9656 KB

Open AccessArticle

Diurnal Analysis of Nor’westers over Gangetic West Bengal as Observed from Weather Radar

by Bibraj Raj, Swaroop Sahoo, N. Puviarasan and V. Chandrasekar

Atmosphere 2025, 16(8), 989; https://doi.org/10.3390/atmos16080989 - 20 Aug 2025

Viewed by 527

Abstract

Intense thunderstorms known as Nor’westers develop in the Eastern and North Eastern parts of India and Bangladesh before the monsoon season (March to May). The associated severe weather can cause extensive damage to property and livestock. This study uses the pre-monsoon volumetric data [...] Read more.

Intense thunderstorms known as Nor’westers develop in the Eastern and North Eastern parts of India and Bangladesh before the monsoon season (March to May). The associated severe weather can cause extensive damage to property and livestock. This study uses the pre-monsoon volumetric data of S-band radar from 2013 to 2018 located in Kolkata to investigate the diurnal variation in the characteristics of the storms over Gangetic West Bengal. The cell initiation, echo top heights, maximum reflectivity, and core convective area are determined by using a flexible feature tracking algorithm (PyFLEXTRKR). The variation of the parameters in diurnal scale is examined from 211,503 individual cell tracks. The distribution of the severe weather phenomena based on radar based thresholds in spatial and temporal scale is also determined. The results show that new cell initiation peaks in the late evening and early morning, displaying bimodal variability. Most of these cells have a short lifespan of 0 to 3 h, with fewer than 5 percent of storms lasting beyond 3 h. The occurrence of hail is much greater in the afternoon due to intense surface heating than at other times. In contrast, the occurrence of lightning is higher in the late evening hours when the cell initiation reaches its peak. The convective rains are generally accompanied by lightning, exhibiting a similar diurnal temporal variability but are more widespread. The findings will assist operational weather forecasters in identifying locations that need targeted observation at certain times of the day to enhance the accuracy of severe weather nowcasting. Full article

(This article belongs to the Section Meteorology)

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16 pages, 12472 KB

Open AccessArticle

Modeling and Accuracy Evaluation of Ionospheric VTEC Across China Utilizing CMONOC GPS/GLONASS Observations

by Fu-Ying Zhu and Chen Zhou

Atmosphere 2025, 16(8), 988; https://doi.org/10.3390/atmos16080988 - 20 Aug 2025

Viewed by 476

Abstract

Accurate estimation of the regional ionospheric model (RIM) is essential for Total electron content and high-precision applications of the Global Navigation Satellite System (GNSS). Utilizing dual-frequency observations from over 250 Crustal Movement Observation Network of China (CMONOC) monitoring stations, which are equipped with [...] Read more.

Accurate estimation of the regional ionospheric model (RIM) is essential for Total electron content and high-precision applications of the Global Navigation Satellite System (GNSS). Utilizing dual-frequency observations from over 250 Crustal Movement Observation Network of China (CMONOC) monitoring stations, which are equipped with both GPS and GLONASS receivers, this study investigates the Vertical Total Electron Content (VTEC) estimation models over the China region and evaluates the estimation accuracy under both GPS-only and GPS+GLONASS configurations. Results indicate that, over the Chinese region, the spherical harmonic reginal ionospheric model (G_SH RIM) and polynomial function reginal ionospheric model (G_Poly RIM) based on single GPS observations demonstrate comparable accuracy with highly consistent spatiotemporal distribution characteristics, showing grid mean deviations of 1.60 TECu and 1.62 TECu, respectively. The combined GPS+GLONASS observation-based RIMs (GR_SH RIM and GR_Poly RIM) significantly improve the TEC modeling accuracy in the Chinese peripheral regions, though the overall average accuracy decreases compared to single-GPS models. Specifically, GR_SH RIM and GR_Poly RIM exhibit mean deviations of 2.15 TECu and 2.32 TECu, respectively. A preliminary analysis reveals that the reduced accuracy is primarily due to the systematic errors introduced by imprecise differential code biases (DCBs) of GLONASS satellites. These findings can provide valuable references for multi-GNSS regional ionospheric estimation. Full article

(This article belongs to the Special Issue Advanced GNSS for Ionospheric Sounding and Disturbances Monitoring)

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16 pages, 2789 KB

Open AccessArticle

Thermal Comfort and Tourism in Mostar (Bosnia and Herzegovina): A Human Bioclimatic Information Sheet for Visitors and Planners

by Milica Pecelj, Slavica Malinović-Milićević and Andreas Matzarakis

Atmosphere 2025, 16(8), 987; https://doi.org/10.3390/atmos16080987 - 20 Aug 2025

Viewed by 887

Abstract

In the context of growing climate change and more frequent heat extremes, tourism in Mediterranean cities like Mostar (Bosnia and Herzegovina) is becoming increasingly vulnerable. This study aimed to provide a detailed analysis of the human bioclimatic conditions in Mostar using the physiologically [...] Read more.

In the context of growing climate change and more frequent heat extremes, tourism in Mediterranean cities like Mostar (Bosnia and Herzegovina) is becoming increasingly vulnerable. This study aimed to provide a detailed analysis of the human bioclimatic conditions in Mostar using the physiologically equivalent temperature (PET) index, the modified PET (mPET), and the Climate-Tourism Information Scheme (CTIS), based on hourly meteorological data for the period 2000–2020. By applying the RayMan model, relevant bioclimatic parameters were calculated for three key times of day (07:00, 14:00, and 21:00 CET), and the results were analyzed in terms of seasonal and daily patterns of thermal stress. The most intense thermal stress was observed during summer afternoon hours, while the transitional seasons (spring and autumn) offer significantly more favorable conditions for tourist activities. A major contribution of this study is the creation of the first integrated bioclimatic information sheet for Mostar, which brings together PET, mPET, and CTIS outputs in accessible format tailored to local tourism needs. It serves as a scientifically based and practical tool for informing visitors and improving the planning of tourism activities in accordance with local climatic characteristics. Due to its visual clarity and ease of interpretation, the information sheet has strong potential for strategic adaptation in climate-sensitive tourism management. Full article

(This article belongs to the Special Issue Climate Change and Tourism: Impacts and Responses)

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26 pages, 5159 KB

Open AccessArticle

Analysis of Carbon Emission Drivers and Climate Mitigation Pathways in the Energy Industry: Evidence from Shanxi, China

by Chen Ning, Jiangping Li, Jingyi Shen, Yunxin Lei, Ting Li, Yanan Zhang and Gaiyan Yang

Atmosphere 2025, 16(8), 986; https://doi.org/10.3390/atmos16080986 - 19 Aug 2025

Viewed by 545

Abstract

In the context of global warming and China’s “dual carbon” goals, Shanxi, as China’s main coal-producing region (accounting for 28.4% of the country’s coal production), is facing the dual challenges of carbon emission reduction and economic development. Based on the data from 1990 [...] Read more.

In the context of global warming and China’s “dual carbon” goals, Shanxi, as China’s main coal-producing region (accounting for 28.4% of the country’s coal production), is facing the dual challenges of carbon emission reduction and economic development. Based on the data from 1990 to 2019, this study quantitatively analysed the carbon emission driving mechanisms of seven major energy sources in Shanxi, including coal, coke, and gasoline, through the coupling analysis of the Kaya identity and the LMDI model, and explored the climate change mitigation pathways. The results show that the total carbon emissions of Shanxi’s energy sector increased significantly from 1990 to 2019, with coal being the most important emission source. Through the decomposition of the LMDI model, it is found that the effect of economic activity is the core driving force of carbon emission growth, and the improvement of energy intensity is the key inhibitor. It is worth noting that the demographic effect turned negative after 2010, which had a dampening effect on the growth of carbon emissions. In addition, the adjustment of energy structure shows the characteristics of stages: the structural effect of coal has turned from negative to positive after 2010, while the proportion of clean energy, such as natural gas, has increased, indicating that the optimisation of energy structure has achieved initial results. Based on the above findings, the study proposes three major paths for climate mitigation in Shanxi’s energy industry: (1) promote low-carbon upgrading of the industry and reduce the economy’s dependence on high-carbon energy; (2) Strengthen energy efficiency and continuously reduce energy consumption per unit of GDP through technological innovation; (3) accelerate the transformation of the energy structure and expand the proportion of clean energy such as natural gas and renewable energy. This paper innovatively provides an empirical reference for the model-based, coupling-based carbon emissions-driven analysis and climate mitigation strategy design in resource-based areas. Full article

(This article belongs to the Section Climatology)

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19 pages, 5844 KB

Open AccessArticle

Cloud Particle Detection in 2D-S Imaging Data via an Adaptive Anchor SSD Model

by Shuo Liu, Dingkun Yang and Luhong Fan

Atmosphere 2025, 16(8), 985; https://doi.org/10.3390/atmos16080985 - 19 Aug 2025

Viewed by 466

Abstract

The airborne 2D-S optical array probe has worked for more than ten years and has collected a large number of cloud particle images. However, existing detection methods cannot detect cloud particles with high precision due to the size differences of cloud particles and [...] Read more.

The airborne 2D-S optical array probe has worked for more than ten years and has collected a large number of cloud particle images. However, existing detection methods cannot detect cloud particles with high precision due to the size differences of cloud particles and the occurrence of particle fragmentation during imaging. So, this paper proposes a novel cloud particle detection method. The key innovation is an adaptive anchor SSD module, which overcomes existing limitations by generating anchor points that adaptively align with cloud particle size distributions. Firstly, morphological transformations generate multi-scale image information through repeated dilation and erosion operations, while removing irrelevant artifacts and fragmented particles for data cleaning. After that, the method generates geometric and mass centers across multiple scales and dynamically merges these centers to form adaptive anchor points. Finally, a detection module integrates a modified SSD with a ResNet-50 backbone for accurate bounding box predictions. Experimental results show that the proposed method achieves an mAP of 0.934 and a recall of 0.905 on the test set, demonstrating its effectiveness and reliability for cloud particle detection using the 2D-S probe. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Atmospheric Sciences)

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18 pages, 4563 KB

Open AccessArticle

Dynamic Characteristics of Key Meteorological Elements and Their Impacts on Major Crop Yields in Albic Soil Region of Sanjiang Plain in China

by Jingyang Li, Huanhuan Li, Qiuju Wang, Qingying Meng, Jiahe Zou, Yu Jiang and Chunwei Zhou

Atmosphere 2025, 16(8), 984; https://doi.org/10.3390/atmos16080984 - 19 Aug 2025

Viewed by 539

Abstract

The vulnerability of regional agricultural systems continues to intensify under the influence of global climate change. Understanding the spatiotemporal variation in meteorological elements and their agricultural response mechanisms has become a critical scientific challenge for ensuring food security. This study focuses on the [...] Read more.

The vulnerability of regional agricultural systems continues to intensify under the influence of global climate change. Understanding the spatiotemporal variation in meteorological elements and their agricultural response mechanisms has become a critical scientific challenge for ensuring food security. This study focuses on the 852 Farm in the typical area of the albic soil region on the Sanjiang Plain in China. This research integrates multi-source meteorological observations and crop yield data from 2001 to 2024. Using methods such as wavelet analysis, grey relational analysis, and cross-wavelet analysis, this study systematically investigates the dynamic changes and cyclical evolution patterns of key meteorological factors and their impact on the yields of different staple crops. The results indicate that, in terms of trend evolution, air temperature, relative humidity, and surface temperature show no significant upward trend (Z > 0; p > 0.05), while precipitation significantly increases (Z > 0; p < 0.05). Evaporation and sunlight show a nonsignificant downward trend (Z < 0; p > 0.05). The yields of rice, soybean, and corn generally exhibit fluctuating upward trends (Z > 0; p > 0.05). In terms of periodic coupling characteristics, meteorological factors exhibit multi-time-scale oscillations at 22a, 12a, and 8a. The yields of the three staple crops form significant time–frequency couplings with meteorological factors in the 22a and 8a periods. Regarding the correlation, air temperature demonstrates the highest grey correlation degree (γ ≥ 0.8) and strong coherence with crop yields, followed by precipitation and sunlight. These findings provide a theoretical and quantitative basis for understanding the multi-scale interactive mechanisms of climate adaptation in agricultural systems of the albic soil region, as well as for managing and optimizing climate-resilient farming practices. Full article

(This article belongs to the Section Meteorology)

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23 pages, 3742 KB

Open AccessArticle

Emergency Medical Interventions in Areas with High Air Pollution: A Case Study from Małopolska Voivodeship, Poland

by Ewa Szewczyk, Michał Lupa, Mateusz Zaręba, Elżbieta Węglińska, Tomasz Danek and Amit Kumar Mishra

Atmosphere 2025, 16(8), 983; https://doi.org/10.3390/atmos16080983 - 18 Aug 2025

Viewed by 732

Abstract

Air pollution poses a significant threat to public health, particularly in urban and industrialized regions. This study investigates the relationship between air quality and the frequency of Emergency Medical Service (EMS) calls in the Małopolska Voivodeship of Poland between 2020 and 2023. Data [...] Read more.

Air pollution poses a significant threat to public health, particularly in urban and industrialized regions. This study investigates the relationship between air quality and the frequency of Emergency Medical Service (EMS) calls in the Małopolska Voivodeship of Poland between 2020 and 2023. Data from over 190 air quality sensors (PM₁₀) were spatially aggregated using both hexagonal grids and administrative boundaries, while EMS call records were filtered to focus on cardiovascular and respiratory incidents. During 2020–2023, a total of 305,142 EMS calls were analyzed, and months with PM₁₀ exceedances showed an average of 1.50 respiratory calls per 1000 residents compared to 1.19 in months without exceedances. Statistical analyses, including Kolmogorov-Smirnov tests and Pearson correlation, were applied to explore temporal and spatial associations. Results indicate a statistically significant increase in EMS calls during periods of elevated air pollution, with the strongest correlation observed for respiratory-related incidents. Comparative analyses between high- and low-pollution municipalities supported the observed relationships. Further analysis indicated that the COVID-19 pandemic may have partially confounded these associations, particularly for respiratory cases, though significant patterns remained even after accounting for pandemic peaks. While limitations related to data gaps and seasonal biases exist, the findings suggest that real-time air pollution data could inform better EMS resource allocation. This research highlights the potential of integrating environmental data into public health strategies to improve emergency response and reduce health risks in polluted regions. Full article

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

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28 pages, 5927 KB

Open AccessArticle

Aerosols in Northern Morocco (Part 4): Seasonal Chemical Signatures of PM_2.5 and PM₁₀

by Abdelfettah Benchrif, Mounia Tahri, Otmane Khalfaoui, Bouamar Baghdad, Moussa Bounakhla and Hélène Cachier

Atmosphere 2025, 16(8), 982; https://doi.org/10.3390/atmos16080982 - 18 Aug 2025

Viewed by 530

Abstract

Atmospheric aerosols are recognized as a major air pollutant with significant impacts on human health, air quality, and climate. Yet, the chemical composition and seasonal variability of aerosols remain underexplored in several Western Mediterranean regions. This study presents a year-long investigation of PM [...] Read more.

Atmospheric aerosols are recognized as a major air pollutant with significant impacts on human health, air quality, and climate. Yet, the chemical composition and seasonal variability of aerosols remain underexplored in several Western Mediterranean regions. This study presents a year-long investigation of PM_2.5 and PM₁₀ in Tetouan, Northern Morocco, where both local emissions and regional transport influence air quality. PM_2.5 and PM₁₀ samples were collected and analysed for total mass and comprehensive chemical characterization, including organic carbon (OC), elemental carbon (EC), water-soluble ions (WSIs), and sugar tracers (levoglucosan, arabitol, and glucose). Concentration-weighted trajectory (CWT) modelling and air mass back-trajectory analyses were used to assess potential source regions and transport pathways. PM_2.5 concentrations ranged from 4.2 to 41.8 µg m⁻³ (annual mean: 18.0 ± 6.4 µg m⁻³), while PM₁₀ ranged from 11.9 to 66.3 µg m⁻³ (annual mean: 30.8 ± 9.7 µg m⁻³), with peaks in winter and minima in spring. The PM_2.5-to-PM₁₀ ratio averaged 0.59, indicating a substantial accumulation of particle mass within the fine fraction, especially during the cold season. Carbonaceous aerosols dominated the fine fraction, with total carbonaceous aerosol (TCA) contributing ~52% to PM_2.5 and ~34% to PM₁₀. Secondary organic carbon (SOC) accounted for up to 90% of OC in PM_2.5, reaching 7.3 ± 3.4 µg m⁻³ in winter. WSIs comprised ~39% of PM_2.5 mass, with sulfate, nitrate, and ammonium as major components, peaking in summer. Sugar tracers exhibited coarse-mode dominance, reflecting biomass burning and biogenic activity. Concentration-weighted trajectory and back-trajectory analyses identified the Mediterranean Basin and Iberian Peninsula as dominant source regions, in addition to local urban emissions. Overall, this study attempts to fill a critical knowledge gap in Southwestern Mediterranean aerosol research by providing a comprehensive characterization of PM_2.5 and PM₁₀ chemical composition and their seasonal dynamics in Tetouan. It further offers new insights into how a combination of local emissions and regional transport shapes the aerosol composition in this North African urban environment. Full article

(This article belongs to the Special Issue Atmospheric Aerosol Pollution)

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18 pages, 1987 KB

Open AccessArticle

Toledo and Climate Change: 30 Years of Clinical Aerobiology in the Center of Spain

by Angel Moral de Gregorio, Raúl Guzmán Rodríguez, Carlos Senent Sánchez, Francisco Feo Brito and Pedro Beneyto Martin

Atmosphere 2025, 16(8), 981; https://doi.org/10.3390/atmos16080981 - 18 Aug 2025

Viewed by 529

Abstract

The incidence of allergic diseases has increased notably in recent years. The reasons for this increase include air pollution, diet, and infectious factors. This study aims to analyze the interactions between aeroallergens, environmental pollutants, and meteorological factors and their impact on allergenic sensitization [...] Read more.

The incidence of allergic diseases has increased notably in recent years. The reasons for this increase include air pollution, diet, and infectious factors. This study aims to analyze the interactions between aeroallergens, environmental pollutants, and meteorological factors and their impact on allergenic sensitization in Toledo, Spain. An aerobiological study was conducted over the past 30 years (1994–2023) using a Burkard collector and the SEAIC (Spanish Society of Allergology and Clinical Immunology) methodology. Meteorological data were obtained from the State Meteorological Agency (AEMET) and pollutant data were acquired from the Castilla-La Mancha Air Quality Monitoring Network. Patients presenting with seasonal allergic symptoms at the University Hospital of Toledo were selected for skin testing with various types of airborne pollen. A total of twenty pollen taxa were identified in the Toledo atmosphere, as follows: Cupressaceae (26.53%); Olea europaea (21.62%); Quercus (21.12%); Poaceae (10.30%); Urticaceae (2.58%); Plantago (2.48%); Platanus (2.00%); Amaranthaceae (1.72%); Rumex (1.68%); and Morus, Pistacia, Populus, Artemisia, Fraxinus, Alnus, Carex, and Ericaceae (less than 1% each). The average temperature increased by 1.2 °C, while the level of precipitation remained stable. Among all pollutants, only a moderate increase in ozone levels was observed; however, the concentrations of particulate matter and nitrogen oxides decreased. The prevalence of pollen sensitization in allergic patients ranged from 8% for Pinus nigra to 84% for Phleum pratense. In conclusion, the rise in temperature due to climate change, coupled with high concentrations of pollutants such as ozone, can result in increased concentrations of the main types of wind-borne pollen. Thus, this can lead to a greater sensitivity to pollen and, consequently, more people becoming allergic to pollen. Full article

(This article belongs to the Special Issue Characterization and Toxicity of Atmospheric Pollutants)

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18 pages, 8063 KB

Open AccessArticle

Concentration Characteristics, Source Analysis, and Health Risk Assessment of Water-Soluble Heavy Metals in PM_2.5 During Winter in Taiyuan, China

by Qingyu Hu, Chao Zhang, Yang Chen, Nan Pei, Yufeng Zhao, Lijuan Sun, Jie Lan, Fengxian Liu, Ziyong Guo, Ling Mu, Jiancheng Wang and Xinhui Bi

Atmosphere 2025, 16(8), 980; https://doi.org/10.3390/atmos16080980 - 17 Aug 2025

Viewed by 780

Abstract

To address the research gap on water-soluble heavy metals (WSHMs) in Taiyuan, China, we conducted a winter campaign (18–29 January 2019) at an urban site to measure fifteen WSHMs (Zn, Fe, Mn, Ba, Cu, Se, As, Sb, Sn, Pb, Ni, V, Ti, Cd, [...] Read more.

To address the research gap on water-soluble heavy metals (WSHMs) in Taiyuan, China, we conducted a winter campaign (18–29 January 2019) at an urban site to measure fifteen WSHMs (Zn, Fe, Mn, Ba, Cu, Se, As, Sb, Sn, Pb, Ni, V, Ti, Cd, and Co). The mean concentration of total WSHMs (∑WSHMs) in PM_2.5 was 209.17 ± 187.21 ng m⁻³. Notably, the mass concentrations of ∑WSHMs on heavy pollution days (291.01 ± 170.64 ng m⁻³) were 224.8% higher than those on mild pollution days (89.61 ± 55.36 ng m⁻³). Principal component analysis (PCA) was applied in combination with absolute principal component score–multiple linear regression (APCS-MLR) to analyze pollution sources and their contributions. The results showed that the main sources of pollution were coal combustion and vehicle emissions (42.50%), along with the metallurgical industry and natural dust (34.47%). The carcinogenic and non-carcinogenic risks of WSHMs were assessed for both adults and children based on the United States Environmental Protection Agency’s (U.S. EPA) assessment guidelines and the International Agency for Research on Cancer (IARC) database. Children faced higher non-carcinogenic risks (hazard index = 2.37) than adults (hazard index = 0.30), exceeding the safety threshold (hazard index = 1). The total carcinogenic risk reached 2.20 × 10⁻⁵, exceeding the threshold value (1 × 10⁻⁶) for carcinogenic risk. Water-soluble arsenic (As) dominated both carcinogenic and non-carcinogenic risks in winter and was the riskiest element. These findings provide an essential basis for controlling PM_2.5-bound WSHMs in industrialized areas. Full article

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

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24 pages, 8188 KB

Open AccessArticle

Top of the Atmosphere Reflected Shortwave Radiative Fluxes from ABI on GOES-18

by Yingtao Ma, Rachel T. Pinker, Wen Chen, Istvan Laszlo, Hye-Yun Kim, Hongqing Liu and Jaime Daniels

Atmosphere 2025, 16(8), 979; https://doi.org/10.3390/atmos16080979 - 17 Aug 2025

Viewed by 546

Abstract

In this study, we describe the derivation and evaluation of Top of the Atmosphere (TOA) Shortwave Radiative (SWR) Fluxes from the Advanced Baseline Imager (ABI) sensor on the GOES-18 satellite. The TOA estimates use narrowband observations from ABI that are transformed to broadband [...] Read more.

In this study, we describe the derivation and evaluation of Top of the Atmosphere (TOA) Shortwave Radiative (SWR) Fluxes from the Advanced Baseline Imager (ABI) sensor on the GOES-18 satellite. The TOA estimates use narrowband observations from ABI that are transformed to broadband (NTB), based on simulations and adjusted to total fluxes using Angular Distribution Models (ADMs). Subsequently, the GOES-18 estimates are evaluated against the Clouds and the Earth’s Radiant Energy System (CERES) data, the only observed SWR broadband flux dataset. The importance of agreement at the TOA is that most methodologies to derive surface SWR start with the satellite observation at the TOA. Moreover, information needed to compute radiative fluxes at both boundaries (TOA and surface) is needed for estimating the energy absorbed by the atmosphere. The methodology described was comprehensively evaluated, and possible sources of errors were identified. The results of the evaluation for the four seasonal months indicate that by using the best available auxiliary data, the accuracy achieved in estimating TOA SWR at the instantaneous scale ranges between 0.55 and 17.14 W m⁻² for the bias and 22.21 to 30.64 W m⁻² for the standard deviation of biases (differences are ABI minus CERES). It is believed that the high bias of 17.14 for July is related to the predominantly cloudless sky conditions, when the used ADMs do not perform as well as for cloudy conditions. Full article

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

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15 pages, 3219 KB

Open AccessArticle

Dynamic Risk Assessment of Collapse Geological Hazards on Highway Slopes in Basalt Regions During Rainy Seasons

by Lihui Qian, Peng Zhao and Zhongshui Li

Atmosphere 2025, 16(8), 978; https://doi.org/10.3390/atmos16080978 - 17 Aug 2025

Viewed by 567

Abstract

Anchored in the four-factor theory of natural hazard risk, this study presents a dynamic risk assessment of collapse geological hazards (CGHs) using the S3K highway slope in Changbai Korean Autonomous County, China, as a case study. Building on previous research, the methodological framework [...] Read more.

Anchored in the four-factor theory of natural hazard risk, this study presents a dynamic risk assessment of collapse geological hazards (CGHs) using the S3K highway slope in Changbai Korean Autonomous County, China, as a case study. Building on previous research, the methodological framework consists of three sequential stages: (1) critical indicators for CGHs in basalt regions are identified, with iron-staining anomalies—a hallmark of such terrains—innovatively integrated as a slope stability metric; (2) a system dynamics (SD) model is developed in Vensim to quantify dynamic feedback mechanisms, focusing on the “rock weathering–rainfall triggering–slope instability” nexus, and time-varying parameters are introduced to enable monthly-scale risk prediction; and (3) a 500 m × 500 m grid system is established using ArcGIS 10.4, and a computer program is developed to achieve SD-GIS coupling and calculate grid parameters. The information value method is then employed to determine risk thresholds, thereby completing CGH risk assessment and prediction. The results indicate that over the next five years, high-risk areas will exhibit spatial agglomeration when monthly rainfall exceeds approximately 130 mm (July and August). Conversely, when monthly rainfall is below around 60 mm, the entire region will display low or no risk. Model simulations reveal that risks during the rainy season over the next five years will exhibit insignificant variability, prompting simplification of the resultant cartography. Field validation corroborates the robustness of the model. This research overcomes the primary limitations of conventional static assessment models by improving the dynamic predictability and the applicability to basalt terrains. The integrated SD-GIS framework presents a novel methodological paradigm for dynamic CGH risk analysis and offers support for the formulation of targeted disaster mitigation strategies. Full article

(This article belongs to the Section Climatology)

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19 pages, 3981 KB

Open AccessArticle

Dataset Construction for Radiative Transfer Modeling: Accounting for Spherical Curvature Effect on the Simulation of Radiative Transfer Under Diverse Atmospheric Scenarios

by Qingyang Gu, Kun Wu, Xinyi Wang, Qijia Xin and Luyao Chen

Atmosphere 2025, 16(8), 977; https://doi.org/10.3390/atmos16080977 - 17 Aug 2025

Viewed by 569

Abstract

Conventional radiative transfer (RT) models often adopt the plane-parallel (PP) approximation, which neglects Earth’s curvature and leads to significant optical path errors under large solar or sensor zenith angles, particularly for high-latitude regions and twilight conditions. The spherical Monte Carlo method offers high [...] Read more.

Conventional radiative transfer (RT) models often adopt the plane-parallel (PP) approximation, which neglects Earth’s curvature and leads to significant optical path errors under large solar or sensor zenith angles, particularly for high-latitude regions and twilight conditions. The spherical Monte Carlo method offers high accuracy but is computationally expensive, and the commonly used pseudo-spherical (PSS) approximation fails when the viewing zenith angle exceeds 80°. With the increasing application of machine learning in atmospheric science, the efficiency and angular limitations of spherical RT simulations may be overcome. This study provides a physical and quantitative foundation for developing a hybrid RT framework that integrates physical modeling with machine learning. By systematically quantifying the discrepancies between PP and spherical RT models under diverse atmospheric scenarios, key influencing factors—including wavelength, solar and viewing zenith angles, aerosol properties (e.g., single scattering albedo and asymmetry factor), and PP-derived radiance—were identified. These variables significantly affect spherical radiative transfer and serve as effective input features for data-driven models. Using the corresponding spherical radiance as the target variable, the proposed framework enables rapid and accurate inference of spherical radiative outputs based on computationally efficient PP simulations. Full article

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

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17 pages, 2321 KB

Open AccessArticle

Variations in the Surface Atmospheric Electric Field on the Qinghai–Tibet Plateau: Observations at China’s Gar Station

by Jia-Nan Peng, Shuai Fu, Yan-Yan Xu, Gang Li, Tao Chen and En-Ming Xu

Atmosphere 2025, 16(8), 976; https://doi.org/10.3390/atmos16080976 - 17 Aug 2025

Viewed by 625

Abstract

The Qinghai-Tibet Plateau, known as the “third pole” of the Earth with an average elevation of approximately 4500 m, offers a unique natural laboratory for probing the dynamic behavior of the global electric circuit. In this study, we conduct a comprehensive analysis of [...] Read more.

The Qinghai-Tibet Plateau, known as the “third pole” of the Earth with an average elevation of approximately 4500 m, offers a unique natural laboratory for probing the dynamic behavior of the global electric circuit. In this study, we conduct a comprehensive analysis of near-surface vertical atmospheric electric field (AEF) measurements collected at the Gar Station (80.1° E, 32.5° N; 4259 m a.s.l.) on the western Tibetan Plateau, spanning the period from November 2021 to December 2024. Fair-weather conditions are imposed. The annual mean AEF at Gar is ∼0.331 kV/m, significantly higher than values observed at lowland and plain sites, indicating a pronounced enhancement in atmospheric electricity associated with high-altitude conditions. Moreover, the AEF exhibits marked seasonal variability, peaking in December (∼0.411–0.559 kV/m) and valleying around July–August (∼0.150–0.242 kV/m), yielding an overall amplitude of approximately 0.3 kV/m. We speculate that this seasonal pattern is primarily driven by variations in aerosol concentration. During winter, increased aerosol loading from residential heating and vehicle emissions due to incomplete combustion reduces atmospheric conductivity by depleting free ions and decreasing ion mobility, thereby enhancing the near-surface AEF. In contrast, lower aerosol concentrations in summer lead to weaker AEF. This seasonal decline in aerosol levels is likely facilitated by stronger winds and more frequent rainfall in summer, which enhance aerosol dispersion and wet scavenging, whereas weaker winds and limited precipitation in winter favor near-surface aerosol accumulation. On diurnal timescales, the Gar AEF curve deviates significantly from the classical Carnegie curve, showing a distinct double-peak and double-trough structure, with maxima at ∼03:00 and 14:00 UT and minima near 00:00 and 10:00 UT. This deviation may partly reflect local influences related to sunrise and sunset. This study presents the longest ground-based AEF observations over the Qinghai–Tibet Plateau, providing a unique reference for future studies on altitude-dependent AEF variations and their coupling with space weather and climate processes. Full article

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24 pages, 3586 KB

Open AccessArticle

Energy Sustainability of Urban Areas by Green Systems: Applied Thermodynamic Entropy and Strategic Modeling Means

by Carla Balocco, Giacomo Pierucci, Michele Baia, Costanza Borghi, Saverio Francini, Gherardo Chirici and Stefano Mancuso

Atmosphere 2025, 16(8), 975; https://doi.org/10.3390/atmos16080975 - 17 Aug 2025

Viewed by 727

Abstract

Global warming, anthropogenic pressure, and urban expansion at the expense of green spaces are leading to an increase in the incidence of urban heat islands, creating discomfort and health issue for citizens. This present research aimed at quantifying the impact of nature-based solutions [...] Read more.

Global warming, anthropogenic pressure, and urban expansion at the expense of green spaces are leading to an increase in the incidence of urban heat islands, creating discomfort and health issue for citizens. This present research aimed at quantifying the impact of nature-based solutions to support decision-making processes in sustainable energy action plans. A simple method is provided, linking applied thermodynamics to physics-informed modeling of urban built-up and green areas, high-resolution climate models at urban scale, greenery modeling, spatial georeferencing techniques for energy, and entropy exchanges evaluation in urban built-up areas, with and without vegetation. This allows the outdoor climate conditions and thermo-hygrometric well-being to improve, reducing the workload of cooling plant-systems in buildings and entropy flux to the environment. The finalization and post-processing of obtained results allows the definition of entropy footprints. The main findings show a decrease in greenery’s contribution for different scenarios, referring to a different climatological dataset, but an increase in entropy that becomes higher for the scenario with higher emissions. The comparison between the entropy footprint values for different urban zones can be a useful support to public administrations, stakeholders, and local governments for planning proactive resilient cities and anthropogenic impact reduction and climate change mitigation. Full article

(This article belongs to the Special Issue Advanced Studies on Climate Change in Urban Areas: Emerging Technologies and Strategies to Address Heat Waves and Improve Thermo-Hygrometric Comfort)

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22 pages, 3221 KB

Open AccessArticle

Exploring NDVI Responses to Regional Climate Change by Leveraging Interpretable Machine Learning: A Case Study of Chengdu City in Southwest China

by Ying Xiang, Guirong Hou, Junjie Li, Yidan Zhang, Jie Lu, Zhexiu Yu, Fabao Niu and Hanqing Yang

Atmosphere 2025, 16(8), 974; https://doi.org/10.3390/atmos16080974 - 17 Aug 2025

Viewed by 670

Abstract

Regional extreme climate change remains a major environmental issue of global concern. However, in the context of the joint effects of urban expansion and the urban ecological environment, the responses of the normalized difference vegetation index (NDVI) to regional climate change and its [...] Read more.

Regional extreme climate change remains a major environmental issue of global concern. However, in the context of the joint effects of urban expansion and the urban ecological environment, the responses of the normalized difference vegetation index (NDVI) to regional climate change and its driving mechanism remain unclear. This study takes Chengdu as an example, selects the air temperature (Ta), precipitation (P), wind speed (WS), and soil water content (SWC) within the period from 2001 to 2023 as influencing factors, and uses Theil-Sen median trend analysis and interpretable machine learning models (random forest (RF), BP neural network, support vector machine (SVM), and extreme gradient boosting (XG-Boost) models). The average absolute value of Shapley additive explanations (SHAPs) is adopted as an indicator to explore the key mechanism driving regional climate change in Chengdu in terms of NDVI changes. The analysis results reveal that the NDVI exhibited an extremely significant increasing trend during the study period (p = 8.6 × 10⁻⁶ < 0.001), and that precipitation showed a significant increasing trend (p = 1.2 × 10⁻⁴ < 0.001); however, the air temperature, wind speed, and soil-relative volumetric water content all showed insignificant increasing trends. A simulation of interpretable machine learning models revealed that the random forest (RF) model performed exceptionally well in terms of simulating the dynamics of the urban NDVI (R² = 0.746), indicating that the RF model has an excellent ability to capture the complex ecological interactions of a city without prior assumptions. The dependence relationship between the simulation results and the main driving factors indicates that the Ta and P are the main factors affecting the NDVI changes. In contrast, the SWC and WS had relatively small influences on the NDVI changes. The prediction analysis results reveal that a monthly average temperature of 25 °C and a monthly average precipitation of approximately 130 mm are conducive to the stability of the NDVI in the study area. This study provides a reference for exploring the responses of NDVI changes to regional climate change in the context of urban expansion and urban ecological construction. Full article

(This article belongs to the Special Issue Vegetation–Atmosphere Interactions in a Changing Climate)

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18 pages, 12874 KB

Open AccessArticle

Diagnosing Tibetan Plateau Summer Monsoon Variability Through Temperature Advection

by Xueyi Xun, Zeyong Hu, Fei Zhao, Zhongqiang Han, Min Zhang and Ruiqing Li

Atmosphere 2025, 16(8), 973; https://doi.org/10.3390/atmos16080973 - 16 Aug 2025

Viewed by 540

Abstract

It has always been a research topic for some meteorologists to design a new and reasonable calculation scheme of the intensity of the Tibetan Plateau (TP) summer monsoon (TPSM). Existing indices are defined based on dynamic factors. However, the intensity of the TPSM [...] Read more.

It has always been a research topic for some meteorologists to design a new and reasonable calculation scheme of the intensity of the Tibetan Plateau (TP) summer monsoon (TPSM). Existing indices are defined based on dynamic factors. However, the intensity of the TPSM can also be influenced by thermal factors. We therefore propose defining a TPMI in terms of horizontal temperature advection within the main body of the TP. This provides a new index that directly quantifies the extent to which the thermal forcing in the TP region regulates the monsoon system. The new index emphasizes the importance of the atmospheric asymmetry structure in measuring TPSM strength, represents the variability of the TPSM circulation system, effectively reflects the meteorological elements, and accurately represents the climate variation. Tropospheric temperature (TT) and TPSM are linked by the new index. These significant centers of correlation are characterized by alternating positive and negative phases along the Eastern European Plain, across the Turan Plain, and into southwestern and northeastern China. The correlation coefficients are found to be significantly out of phase between high and low altitudes in the vertical direction. This research broadens our minds and helps us to develop a new approach to measuring TPSM strength. It can also predict extreme weather events in advance based on TPMI changes, providing a scientific basis for disaster warnings and the management of agriculture and water resources. Full article

(This article belongs to the Section Climatology)

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14 pages, 2723 KB

Open AccessArticle

Real-Time Insights into Indoor Air Quality in University Environments: PM and CO₂ Monitoring

by Dan-Marius Mustață, Daniel Bisorca, Ioana Ionel, Ahmed Adjal and Ramon-Mihai Balogh

Atmosphere 2025, 16(8), 972; https://doi.org/10.3390/atmos16080972 - 16 Aug 2025

Viewed by 750

Abstract

This study presents real-time measurements of particulate matter (PM₁, PM_2.5, PM₁₀) and carbon dioxide (CO₂) concentrations across five university indoor environments with varying occupancy levels and natural ventilation conditions. CO₂ concentrations frequently exceeded the [...] Read more.

This study presents real-time measurements of particulate matter (PM₁, PM_2.5, PM₁₀) and carbon dioxide (CO₂) concentrations across five university indoor environments with varying occupancy levels and natural ventilation conditions. CO₂ concentrations frequently exceeded the 1000 ppm guideline, with peak values reaching 3018 ppm and 2715 ppm in lecture spaces, whereas one workshop environment maintained levels well below limits (mean = 668 ppm). PM concentrations varied widely: PM₁₀ reached 541.5 µg/m³ in a carpeted amphitheater, significantly surpassing the 50 µg/m³ legal daily limit, while a well-ventilated classroom exhibited lower levels despite moderate occupancy (PM₁₀ max = 116.9 µg/m³). Elevated PM values were strongly associated with flooring type and occupant movement, not just activity type. Notably, window ventilation during breaks reduced CO₂ concentrations by up to 305 ppm (p < 1 × 10⁻⁴⁷) and PM₁₀ by over 20% in rooms with favorable layouts. These findings highlight the importance of ventilation strategy, spatial orientation, and surface materials in shaping indoor air quality. The study emphasizes the need for targeted, non-invasive interventions to reduce pollutant exposure in historic university buildings where mechanical ventilation upgrades are often restricted. Full article

(This article belongs to the Special Issue Physical and Chemical Characterization of Particulate Matter: Ambient, Personal, and Indoor Perspectives)

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17 pages, 2613 KB

Open AccessArticle

Synergistic Effects of Ambient PM_2.5 and O₃ with Natural Temperature Variability on Non-Accidental and Cardiovascular Mortality: A Historical Time Series Analysis in Urban Taiyuan, China

by Huan Zhou, Hong Geng, Jingjing Tian, Li Wu, Zhihong Zhang and Daizhou Zhang

Atmosphere 2025, 16(8), 971; https://doi.org/10.3390/atmos16080971 - 15 Aug 2025

Viewed by 513

Abstract

Climate change and air pollution are associated with a range of health outcomes, including cardiovascular and respiratory disease. Evaluation of the synergic effects of air pollution and increasing natural temperature on mortality is important for understanding their potential joint health effects. In this [...] Read more.

Climate change and air pollution are associated with a range of health outcomes, including cardiovascular and respiratory disease. Evaluation of the synergic effects of air pollution and increasing natural temperature on mortality is important for understanding their potential joint health effects. In this study, the modification effects of air temperature on the short-term association of ambient fine particulate matter (PM_2.5) and ozone (O₃) with non-accidental death (NAD) and cardiovascular disease (CVD) mortality were evaluated by using the generalized additive model (GAM) combined with the distributed lag nonlinear model (DLNM) in urban areas of Taiyuan, a representative of energy and heavy industrial cities in Northern China. The data on the daily cause-specific death numbers, air pollutants concentrations, and meteorological factors were collected from January 2013 to December 2019, and the temperature was divided into low (<25th percentile), medium (25–75th percentile), and high (>75th percentile) categories. Significant associations of PM_2.5 and O₃ with NAD and CVD mortality were observed in single-effect analysis. A statistically significant increase in the effect estimates of PM_2.5 and O₃ on NAD and CVD mortality was also observed on high-temperature days. But the associations of those were not statistically significant on medium- and low-temperature days. At the same temperature level, the effects of PM_2.5 and O₃ on the CVD mortality were larger than those on NAD (1.74% vs. 1.21%; 1.67% vs. 0.57%), and the elderly and males appeared to be more vulnerable to both higher temperatures and air pollution. The results suggest that the acute effect of PM_2.5 and O₃ on NAD and CVD mortality in urban Taiyuan was enhanced by increasing temperatures, particularly for the elderly and males. It highlights the importance of reducing PM_2.5 and O₃ exposure in urban areas to reduce the public health burden under the situation of global warming. Full article

(This article belongs to the Special Issue Composition Analysis and Health Effects of Atmospheric Particulate Matter (2nd Edition))

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19 pages, 7660 KB

Open AccessArticle

The Impact of Photochemical Loss on the Source Apportionment of Ambient Volatile Organic Compounds (VOCs) and Their Ozone Formation Potential in the Fenwei Plain, Northern China

by Yanan Tao, Qi Xiong, Yawei Dong, Jiayin Zhang, Lei Cao, Min Zhu, Qiaoqiao Wang and Jianwei Gu

Atmosphere 2025, 16(8), 970; https://doi.org/10.3390/atmos16080970 - 15 Aug 2025

Viewed by 779

Abstract

The Fenwei Plain (FWP), one of China’s most polluted regions, has experienced severe ozone (O₃) pollution in recent years. Volatile organic compounds (VOCs), key O₃ precursors, undergo significant photochemical degradation, yet their loss and the implications for source apportionment and [...] Read more.

The Fenwei Plain (FWP), one of China’s most polluted regions, has experienced severe ozone (O₃) pollution in recent years. Volatile organic compounds (VOCs), key O₃ precursors, undergo significant photochemical degradation, yet their loss and the implications for source apportionment and ozone formation potential (OFP) in this region remain unclear. This study conducted summertime VOC measurements in two industrial cities in the FWP, Hancheng (HC) and Xingping (XP), to quantify photochemical losses of VOCs and assessed their impact on source attribution and OFP with photochemical age-based parameterization methods. Significant VOC photochemical losses were observed, averaging 3.6 ppbv (7.1% of initial concentrations) in HC and 1.9 ppbv (5.6%) in XP, with alkenes experiencing the highest depletion (22–30%). Source apportionment based on both initial (corrected) and observed concentrations revealed that industrial sources (e.g., coking, coal washing, and rubber manufacturing) dominated ambient VOCs. Ignoring photochemical losses underestimated contributions from natural gas combustion and biogenic sources, while it overestimated the secondary source. OFP calculated with lost VOCs (OFP_loss) reached 34 ppbv in HC and 15 ppbv in XP, representing 20% and 25% of OFP based on observed concentrations, respectively, with reactive alkenes accounting for over 90% of OFPloss. The results highlight the importance of accounting for VOC photochemical losses for accurate source identification and developing effective O₃ control strategies in the FWP. Full article

(This article belongs to the Section Air Quality)

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15 pages, 3075 KB

Open AccessArticle

Influence of Atmospheric Circulation on Seasonal Temperatures in Serbia

by Suzana Putniković

Atmosphere 2025, 16(8), 969; https://doi.org/10.3390/atmos16080969 - 15 Aug 2025

Viewed by 639

Abstract

An objective classification scheme by Jenkinson and Collison is applied to the period 1961–2010 to statistically model the temperatures over Serbia. The originally identified 26 weather types (WTs) are reorganised into 10 basic types. This discussion includes the synoptic characteristics, frequency and trends [...] Read more.

An objective classification scheme by Jenkinson and Collison is applied to the period 1961–2010 to statistically model the temperatures over Serbia. The originally identified 26 weather types (WTs) are reorganised into 10 basic types. This discussion includes the synoptic characteristics, frequency and trends of the 10 WTs as well as the trends of seasonal mean, maximum and minimum temperatures in Serbia. In this area, the anticyclonic weather type is predominant throughout the year, and its negative trend is significant in summer and autumn. The relationship between air temperature and atmospheric circulation types is investigated by analysing the mean and anomalies of mean, maximum and minimum temperatures for each individual atmospheric circulation type and by stepwise regression. The multiple regression models developed for six stations using circulation WTs as predictors showed the best performance in modelling winter mean temperatures for Zlatibor and Loznica compared to the other stations, while the models for other seasons proved to be inadequate. Full article

(This article belongs to the Special Issue Air Temperature and Precipitation and Relationship to Atmospheric Circulation (2nd Edition))

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17 pages, 10829 KB

Open AccessArticle

Vertical Profiling of PM₁ and PM_2.5 Dynamics: UAV-Based Observations in Seasonal Urban Atmosphere

by Zhen Zhao, Yuting Pang, Bing Qi, Chi Zhang, Ming Yang and Xuezhu Ye

Atmosphere 2025, 16(8), 968; https://doi.org/10.3390/atmos16080968 - 15 Aug 2025

Viewed by 605

Abstract

Urban particulate matter (PM) pollution critically impacts public health and climate. However, traditional ground-based monitoring fails to resolve vertical PM distribution, limiting understanding of transport and stratification-coupled mechanisms. Vertical profiles collected by an unmanned aerial vehicle (UAV) over Hangzhou, a core megacity in [...] Read more.

Urban particulate matter (PM) pollution critically impacts public health and climate. However, traditional ground-based monitoring fails to resolve vertical PM distribution, limiting understanding of transport and stratification-coupled mechanisms. Vertical profiles collected by an unmanned aerial vehicle (UAV) over Hangzhou, a core megacity in China’s Yangtze River Delta, reveal the spatiotemporal heterogeneity and multi-scale drivers of regional PM pollution during two intensive ten-day campaigns capturing peak pollution scenarios (winter: 17–26 January 2019; summer: 21–30 August 2019). Results show stark seasonal differences: winter PM₁ and PM_2.5 averages were 2.6- and 2.7-fold higher (p < 0.0001) than summer. Diurnal patterns were bimodal in winter and unimodal (single valley) in summer. Vertically consistent PM₁ and PM_2.5 distributions featured sharp morning (08:00) concentration increases within specific layers (winter: 250–325 m; summer: 350–425 m). Analysis demonstrates multi-scale coupling of synoptic systems, boundary layer processes, and vertical wind structure governing pollution. Key mechanisms include a winter “Transport-Accumulation-Reactivation” cycle driven by cold air, and summer typhoon circulation influences. We identify hygroscopic growth triggered by inversion-high humidity coupling and sea-breeze-driven secondary aerosol formation. Leveraging UAV-based vertical profiling over Hangzhou, this study pioneers a three-dimensional dissection of layer-coupled PM dynamics in the Yangtze River Delta, offering a scalable paradigm for aerial–ground networks to achieve precision stratified control strategies in megacities. Full article

(This article belongs to the Special Issue Air Pollution in China (4th Edition))

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18 pages, 4355 KB

Open AccessArticle

The Evaluation of ERA5’s Applicability in Nearshore Western Atlantic Regions During Hurricanes—“ISAIAS” 2020

by Zhiyong Xu, Biyun Guo, Guiting Song, Venkata Subrahmanyam Mantravadi, Wenjing Xu, Cheng Wan and John Sikule Sabuyi

Atmosphere 2025, 16(8), 967; https://doi.org/10.3390/atmos16080967 - 15 Aug 2025

Viewed by 581

Abstract

Hurricanes cause significant destruction, disrupting transportation, and resulting in loss of life and property. High-precision marine meteorological data are essential for understanding hurricanes. ERA5 provides high temporal resolution and global coverage of analytical data; however, the accuracy of the data during hurricanes is [...] Read more.

Hurricanes cause significant destruction, disrupting transportation, and resulting in loss of life and property. High-precision marine meteorological data are essential for understanding hurricanes. ERA5 provides high temporal resolution and global coverage of analytical data; however, the accuracy of the data during hurricanes is uncertain. To investigate the applicability of ERA5 during hurricanes, this study used buoy data as reference values and assessed the applicability of ERA5 sea-surface wind speed (WS), sea-surface temperature (SST), and sea-surface pressure (SSP) during the 2020 Atlantic hurricane “ISAIAS” through spatial distribution and error analysis. The results indicate that there is a positive correlation and consistency between the trends of ERA5 and reference values. The average correlation coefficients for SSP, WS, and SST are 0.953, 0.822, and 0.607, respectively. Nearshore topography has a significant impact on data accuracy, resulting in greater errors compared to open-water areas. This study provides a theoretical basis for the application of ERA5 data during hurricanes. Full article

(This article belongs to the Special Issue Ocean–Atmosphere–Land Interactions and Their Roles in Climate Change (2nd Edition))

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19 pages, 11804 KB

Open AccessArticle

Assessing the Impact of Ammonia Emissions from Mink Farming in Denmark on Human Health and Critical Load Exceedance

by Lise Marie Frohn, Jesper Leth Bak, Jørgen Brandt, Jesper Heile Christensen, Steen Gyldenkærne and Camilla Geels

Atmosphere 2025, 16(8), 966; https://doi.org/10.3390/atmos16080966 - 15 Aug 2025

Viewed by 622

Abstract

In this study, the objective is to assess the impacts of NH₃ emissions from mink farming on human health and nature, which are sensitive to atmospheric nitrogen deposition. The impact-pathway approach is applied to follow the emissions from source to impact on [...] Read more.

In this study, the objective is to assess the impacts of NH₃ emissions from mink farming on human health and nature, which are sensitive to atmospheric nitrogen deposition. The impact-pathway approach is applied to follow the emissions from source to impact on human health in Europe (including Denmark) and from source to critical nitrogen load exceedances for NH₃-sensitive nature in Denmark. The Danish Eulerian Hemispheric Model (DEHM) is used for modelling the air pollution concentrations in Europe and nitrogen depositions on land and water surfaces in Denmark arising from NH₃ emissions from mink farming in Denmark. The Economic Valuation of Air (EVA) pollution model system is applied for deriving the health effects and corresponding socio-economic costs in Denmark and Europe arising from the emissions from mink farming. On a local scale in Denmark, the deposition resulting from the NH₃ emissions from mink farming is modelled using the results from the OML-DEP model at a high resolution to derive the critical nitrogen load exceedances for Danish nature areas sensitive to NH₃. From the analysis of the impacts through human exposure to the air pollutants PM_2.5, NO₂, and O₃, it is concluded that in total, ~60 premature deaths annually in Europe, including Denmark, can be attributed to the emissions of NH₃ to the atmosphere from the mink farming sector in Denmark. This corresponds to annual socio-economic costs on the order of EUR 142 million. From the analysis of critical load exceedances, it is concluded that an exceedance of the critical load of nitrogen deposition of ~14,600 hectares (ha) of NH₃-sensitive nature areas in Denmark can be attributed to NH₃ emissions from mink farming. The cost for restoring nature areas of this size, damaged by eutrophication from excess nitrogen deposition, is estimated to be ~EUR 110 million. In 2020, the mink sector in Denmark was shut down in connection with the COVID-19 pandemic. All mink were culled by order of the Danish Government, and now in 2025, the process of determining the level of financial compensation to the farmers is still ongoing. The socio-economic costs following the impacts on human health in Europe and nitrogen-sensitive nature in Denmark of NH₃ emissions from the now non-existing mink sector can therefore be viewed as socio-economic benefits. In this study, these benefits are compared with the expected level of compensation from the Danish Government to the mink farmers, and the conclusion is that the compensation to the mink farmers breaks even with the benefits from reduced NH₃ emissions over a timescale of ~20 years. Full article

(This article belongs to the Special Issue Effects of Natural and Anthropogenic Factors on Climate and Environment (2nd Edition))

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Open AccessArticle

A Hybrid Air Quality Prediction Model Integrating KL-PV-CBGRU: Case Studies of Shijiazhuang and Beijing

by Sijie Chen, Qichao Zhao, Zhao Chen, Yongtao Jin and Chao Zhang

Atmosphere 2025, 16(8), 965; https://doi.org/10.3390/atmos16080965 - 15 Aug 2025

Viewed by 623

Abstract

Accurate prediction of the Air Quality Index (AQI) is crucial for protecting public health; however, the inherent instability and high volatility of AQI present significant challenges. To address this, the present study introduces a novel hybrid deep learning model, KL-PV-CBGRU, which utilizes Kalman [...] Read more.

Accurate prediction of the Air Quality Index (AQI) is crucial for protecting public health; however, the inherent instability and high volatility of AQI present significant challenges. To address this, the present study introduces a novel hybrid deep learning model, KL-PV-CBGRU, which utilizes Kalman filtering to decompose AQI data into features and residuals, effectively mitigating volatility at the initial stage. For residual components that continue to exhibit substantial fluctuations, a secondary decomposition is conducted using variational mode decomposition (VMD), further optimized by the particle swarm optimization (PSO) algorithm to enhance stability. To overcome the limited predictive capabilities of single models, this hybrid framework integrates bidirectional gated recurrent units (BiGRU) with convolutional neural networks (CNNs) and convolutional attention modules, thereby improving prediction accuracy and feature fusion. Experimental results demonstrate the superior performance of KL-PV-CBGRU, achieving R² values of 0.993, 0.963, 0.935, and 0.940 and corresponding MAE values of 2.397, 8.668, 11.001, and 14.035 at 1 h, 8 h, 16 h, and 24 h intervals, respectively, in Shijiazhuang—surpassing all benchmark models. Ablation studies further confirm the critical roles of both the secondary decomposition process and the hybrid architecture in enhancing predictive accuracy. Additionally, comparative experiments conducted in Beijing validate the model’s strong transferability and consistent outperformance over competing models, highlighting its robust generalization capability. These findings underscore the potential of the KL-PV-CBGRU model as a powerful and reliable tool for air quality forecasting across varied urban settings. Full article

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

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Atmosphere, Volume 16, Issue 8 (August 2025) – 102 articles

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