Atmosphere

21 pages, 3087 KiB

Open AccessArticle

Surface Ozone Variability in Two Contrasting Megacities, Cairo and Paris, and Its Observation from Satellites

by Amira N. Mostafa, Stephane Alfaro, Juan Cuesta, Ibrahim A. Hassan and M. M. Abdel Wahab

Atmosphere 2025, 16(4), 475; https://doi.org/10.3390/atmos16040475 (registering DOI) - 18 Apr 2025

With recognized adverse effects on human health and the environment, surface ozone constitutes a major problem within and downwind of urbanized areas. In this work, we first analyzed 5 years of hourly concentrations of ozone measured in two megacities with contrasting climates: Paris [...] Read more.

With recognized adverse effects on human health and the environment, surface ozone constitutes a major problem within and downwind of urbanized areas. In this work, we first analyzed 5 years of hourly concentrations of ozone measured in two megacities with contrasting climates: Paris and Cairo. In both cases, the maximal daily concentrations were observed in summer and they exceeded the 35 ppb threshold recommended by the World Health Organization in 45% and 69% of the days, respectively. During periods of forced reduced activities, these concentrations decreased in Cairo but not in Paris. This indicates that low-emission zones are not necessarily effective to help curb the ozone problem. In a second stage, the ozone retrievals of two satellite-based atmospheric sounding methods (AIRS, and the multispectral approach IASI+GOME2) were compared to the surface measurements. A systematic overestimation, larger for AIRS than IASI+GOME2, was observed. This is likely linked to the fact that satellite approaches retrieve ozone concentrations at higher atmospheric levels than the surface. However, a significantly high linear correlation was obtained at the monthly temporal resolution. Therefore, shift adjustments of the satellite measurements provide efficient proxies of surface observations with significant monthly correlations. This may help complete lacunar surface measurements. Full article

(This article belongs to the Special Issue Ozone Evolution in the Past and Future (2nd Edition))

16 pages, 17622 KiB

Open AccessArticle

Knowledge Map-Based Analysis of Carbon Sequestration Research Dynamics in Forest and Grass Systems: A Bibliometric Analysis

by Quanlin Ma, Xinyou Wang, Baoru Mo, Zaiguo Liu, Yangjun Zhang, Wenzheng Zong and Meiting Bai

Atmosphere 2025, 16(4), 474; https://doi.org/10.3390/atmos16040474 (registering DOI) - 18 Apr 2025

Abstract

Forest and grass systems are globally significant carbon-sequestering ecosystems, crucial for mitigating climate change and optimizing ecological management. To clarify the research history, major contributing groups, and research hotspots related to carbon sequestration in global forest and grass systems, this study utilizes the [...] Read more.

Forest and grass systems are globally significant carbon-sequestering ecosystems, crucial for mitigating climate change and optimizing ecological management. To clarify the research history, major contributing groups, and research hotspots related to carbon sequestration in global forest and grass systems, this study utilizes the core ensemble of the Web of Science database as its data source. Employing bibliometric methodology and software, such as VOSviewer 1.6.20 and CiteSpace 5.7.R1, we analyzed the development of 594 relevant publications from 2010 to 2024, focusing on their developmental lineage, research groups, current research status, and visualizing and analyzing research hotspots and frontiers. The results indicate that the volume of the literature on carbon sequestration in forest and grass systems generally follows the pattern of a logistic growth curve, demonstrating an upward trend from 2010 to 2024. The primary contributors consist of 400 researchers, including Nath, Arun Jyoti, and Ajit, as well as 378 research organizations across 42 countries, including China, the USA, and India. China’s contribution to this field is rapidly increasing, accounting for over 20% of the total articles, with ‘Chinese Acad Sci’ and ‘Univ Chinese Acad Sci’ being the most prominent contributors, together representing 10.45% of the total publications in this field. The 179 journals, including Agroforestry Systems and Forests, serve as a significant platform for academic exchange in the development of this field. The predominant research directions are found in the areas of ‘Environmental Sciences & Ecology’ and ‘Agriculture’, which collectively account for over 50% of the publications. Additionally, research focused on ‘Sequestration’ is increasingly examining the relationship between carbon sequestration in forest and grassland systems and factors such as climate change, ecosystem productivity, and biodiversity. The keyword clusters ‘#0 ferralsol’ and ‘#4 forest ecosystem’ have consistently represented important research directions throughout this period. A total of 21 keywords were identified, with ‘land use change’ exhibiting the highest intensity at 4.4524. Future research should not only prioritize the integration of the impacts of global climate change but also enhance collaboration among authors and institutions. Furthermore, it is essential to promote multidisciplinary and cross-regional collaborative innovations by leveraging emerging technologies such as AI and genetic engineering. Full article

(This article belongs to the Special Issue Forest Ecosystems in a Changing Climate)

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15 pages, 1785 KiB

Open AccessArticle

Typhoon-Induced High PM10 Concentration Events in South Korea: A Comprehensive Analysis of Pre-, During, and Post-Typhoon Periods

by Hana Na and Woo-Sik Jung

Atmosphere 2025, 16(4), 473; https://doi.org/10.3390/atmos16040473 - 18 Apr 2025

Abstract

This study challenges the commonly held belief that typhoons universally improve air quality by dispersing pollutants, offering new insights into their complex effects on PM10 concentrations. Through a comprehensive analysis of long-term data (2001–2021) from seven major South Korean cities, we demonstrate that [...] Read more.

This study challenges the commonly held belief that typhoons universally improve air quality by dispersing pollutants, offering new insights into their complex effects on PM10 concentrations. Through a comprehensive analysis of long-term data (2001–2021) from seven major South Korean cities, we demonstrate that typhoons can lead to significant increases in PM10 concentrations, particularly before and after their passage, under specific meteorological conditions. Contrary to the prevailing assumption, PM10 levels often rise before typhoons due to atmospheric stagnation, and after typhoons due to subsidence and long-range pollutant transport. Our results indicate that the post-typhoon period is particularly prone to high-PM10 events, with PM10 concentrations increasing by 84.5% in Incheon, 60.8% in Busan, and 62.3% in Gwangju. A case study of Typhoon MITAK revealed that pre-typhoon atmospheric conditions contributed to PM10 concentrations exceeding 81 μg/m³ in Seoul, a level classified as ‘unhealthy’ by Korean air quality standards. These findings challenge existing perceptions and provide essential insights into the complex relationship between typhoons and air pollution. The study underscores the importance of understanding the nuanced dynamics of typhoon-induced air pollution and its implications for air quality management, particularly in the context of ongoing climate change and urbanization. Moreover, the integration of real-time monitoring data into predictive air quality models could enhance the ability to mitigate the adverse effects of typhoon-induced air pollution in vulnerable regions. Full article

(This article belongs to the Section Meteorology)

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15 pages, 6167 KiB

Open AccessArticle

Comparison of Sensors for Air Quality Monitoring with Reference Methods in Zagreb, Croatia

by Silvije Davila, Marija Jelena Lovrić Štefiček, Ivan Bešlić, Gordana Pehnec, Marko Marić and Ivana Hrga

Atmosphere 2025, 16(4), 472; https://doi.org/10.3390/atmos16040472 - 18 Apr 2025

Abstract

Within the scope of “Eco Map of Zagreb” project, eight sensor sets (type AQMeshPod) were set up at an automatic measuring station at the Institute for Medical Research and Occupational Health (IMROH) for comparison with reference methods for air quality measurement during 2018. [...] Read more.

Within the scope of “Eco Map of Zagreb” project, eight sensor sets (type AQMeshPod) were set up at an automatic measuring station at the Institute for Medical Research and Occupational Health (IMROH) for comparison with reference methods for air quality measurement during 2018. This station is a city background station within the Zagreb network for air quality monitoring, where measurements of SO₂, CO, NO₂, O₃, PM₁₀ and PM_2.5, are performed using standardized methods accredited according to EN ISO/IEC 17025. This paper presents a comparison of pollutant mass concentrations determined by sensors with reference methods. The data were compared and filtered to remove outliers and handle deviations between the results obtained by sensors and reference methods, considering the different approaches to gas and PM data. A comparison of sensor results with the reference methods showed a large scattering of all gaseous pollutants while the comparison for PM₁₀ and PM_2.5 indicated a satisfactory low dispersion. The results of a regression analysis showed a significant seasonal dependence for all pollutants. Significant statistical differences between the reference methods and sensors for the whole year and in all seasons for all gas pollutants, as well as for PM₁₀, were observed, while for PM_2.5 statistical significance showed varying results. Full article

(This article belongs to the Special Issue Feature Papers in Atmospheric Techniques, Instruments, and Modeling)

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17 pages, 992 KiB

Open AccessArticle

Research on the Threshold Effect of Green Technology Innovation on Fog–Haze Pollution in the Transfer of Air Pollution-Intensive Industries: A Perspective of Thermal Power

by Jingkun Zhou and Yating Li

Atmosphere 2025, 16(4), 471; https://doi.org/10.3390/atmos16040471 - 18 Apr 2025

Abstract

Green technology innovation can effectively reduce the problem of pollution transfer in air pollution-intensive industries like thermal power and realize the green development of air pollution-intensive industries like thermal power. Based on green technology innovation, this paper analyzes the spatial–temporal characteristics of fog–haze [...] Read more.

Green technology innovation can effectively reduce the problem of pollution transfer in air pollution-intensive industries like thermal power and realize the green development of air pollution-intensive industries like thermal power. Based on green technology innovation, this paper analyzes the spatial–temporal characteristics of fog–haze in 31 provinces and municipalities. Taking the panel data of 31 provinces, municipalities, and autonomous regions from 2000 to 2017 as samples, this paper adopts the panel threshold regression method to examine the relationship between green technology innovation and fog–haze pollution in the transfer of air pollution-intensive industries like thermal power. The study found the following: China’s haze outbreak and the subsequent increasingly serious reasons for the implementation of weight detection haze policy seriously misled the haze prevention and control work, simple disorganized management aggravated the degree of haze pollution, and layer by layer, management methods caused the huge increase in secondary particulate matter; haze pollution aggregation occurs in the area of environmental self-purification capacity in the low air pollution-intensive industrial agglomeration to affect the atmospheric environment, a significant increase in the neighbouring industrial pollution agglomeration in resource-rich provinces; green technology innovation above the threshold has a significant inhibitory effect on the industrial transfer of haze pollution, and so on. There is a need for the scientific planning of pollution industry transfer to undertake the development of the place, the effective transfer of Beijing–Tianjin–Hebei haze pollution and other areas of air pollution-intensive industries, the development of targeted green technology innovation to strengthen policies, the scientific management of haze pollution, and the contribution of the scientific management of haze pollution in China. Full article

(This article belongs to the Topic Modelling and Management of Environment, Energy and Resources: Methods, Applications, and Challenges)

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18 pages, 9721 KiB

Open AccessArticle

A Multi-Year Investigation of Thunderstorm Activity at Istanbul International Airport Using Atmospheric Stability Indices

by Oğuzhan Kolay, Bahtiyar Efe, Emrah Tuncay Özdemir and Zafer Aslan

Atmosphere 2025, 16(4), 470; https://doi.org/10.3390/atmos16040470 - 17 Apr 2025

Abstract

Thunderstorms are weather phenomena that comprise thunder and lightning. They typically result in heavy precipitation, including rain, snow, and hail. Thunderstorms have adverse effects on flight at both the ground and the upper levels of the troposphere. The characteristics of the thunderstorm of [...] Read more.

Thunderstorms are weather phenomena that comprise thunder and lightning. They typically result in heavy precipitation, including rain, snow, and hail. Thunderstorms have adverse effects on flight at both the ground and the upper levels of the troposphere. The characteristics of the thunderstorm of İstanbul International Airport (International Civil Aviation Organization (ICAO) code: LTFM) have been investigated because it is currently one of the busiest airports in Europe and the seventh-busiest airport in the world. Geopotential height (m), temperature (°C), dewpoint temperature (°C), relative humidity (%), mixing ratio (g kg⁻¹), wind direction (°), and wind speed (knots) data for the ground level and upper levels of the İstanbul radiosonde station were obtained from the Turkish State Meteorological Service (TSMS) for 29 October 2018 and 1 January 2023. Surface data were regularly collected by the automatic weather stations near the runway and the upper-level data were collected by the radiosonde system located in the Kartal district of İstanbul. Thunderstorm statistics, stability indices, and meteorological variables at the upper levels were evaluated for this period. Thunderstorms were observed to be more frequent during the summer, with a total of 51 events. June had the highest number of thunderstorm events with a total of 32. This averages eight events per year. A total of 72.22% occurred during trough and cold front transitions. The K index and total totals index represented the thunderstorm events better than other stability indices. In total, 75% of the thunderstorm days were represented by these two stability indices. The results are similar to the covering of this area: the convective available potential energy (CAPE) values which are commonly used for atmospheric instability are low during thunderstorm events, and the K and total totals indices are better represented for thunderstorm events. This study investigates thunderstorm events at the LTFM, providing critical insights into aviation safety and operational efficiency. The research aims to improve flight planning, reduce weather-related disruptions, and increase safety and also serves as a reference for airports with similar climatic conditions. Full article

(This article belongs to the Special Issue Weather and Climate Extremes: Past, Current and Future)

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13 pages, 2116 KiB

Open AccessArticle

Effects of Exposure to Air Pollution and Cold Weather on Acute Myocardial Infarction Mortality

by Yu-Hsuan Chen, I-Hsing Liu, Chih-Chun Hsiao, Chun-Gu Cheng and Chun-An Cheng

Atmosphere 2025, 16(4), 469; https://doi.org/10.3390/atmos16040469 - 17 Apr 2025

Abstract

(1) Background: Human exposure to air pollution may induce inflammation and oxidative stress. In addition, extreme air temperatures and relative humidity cause vasoconstriction and abnormal blood cell function. These harmful effects may increase cardiovascular disease mortality. The effects of air pollution and climatic [...] Read more.

(1) Background: Human exposure to air pollution may induce inflammation and oxidative stress. In addition, extreme air temperatures and relative humidity cause vasoconstriction and abnormal blood cell function. These harmful effects may increase cardiovascular disease mortality. The effects of air pollution and climatic factors on mortality in patients with acute myocardial infarction (AMI) are relatively unknown. (2) Methods: We used AMI mortality data collected from Taiwan’s Medical Quality Indicator. Air pollutant data were collected from the Taiwanese Environmental Protection Administration, and air temperature and relative humidity data were obtained from the Taiwanese Central Weather Administration. The effects were estimated using Poisson regression to analyze the relative risk (RR) of mortality from AMI associated with exposure to air pollutants and climatic factors. (3) Results: The RR for every 4.7 μg/m³ increase in particulate matter with a diameter less than 2.5 μm (PM_2.5) was 1.086 (95% CI: 1.033–1.142, p = 0.001). The RR for every 10.3 ppb increase in ozone concentration was 1.095 (95% CI: 1.011–1.185, p = 0.025). The RR for every 6.5 °C decrease in air temperature was 1.087 (95% CI: 1.024–1.154, p = 0.006) for AMI mortality. (4) Conclusions: This study revealed that higher PM_2.5 and ozone concentrations, along with cold weather, are associated with mortality in individuals with AMI. The government must develop policies to promote air pollution prevention, mitigate air pollution, and alert residents about poor air quality and cold weather, thereby promoting sustainable human health. Full article

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

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22 pages, 4791 KiB

Open AccessArticle

Investigation into the Best Available Moisture Pretreatment Approach for the Measurement of Trichloroethylene and Nitrous Oxide Emitted from Semiconductor Industries

by Da-Hyun Baek, Byeong-Gyu Park, Sang-Woo Lee, Trieu-Vuong Dinh and Jo-Chun Kim

Atmosphere 2025, 16(4), 468; https://doi.org/10.3390/atmos16040468 - 17 Apr 2025

Abstract

In this study, the effects of various moisture pretreatment devices (MPDs) on the analytical process of trichloroethylene (TCE) and nitrous oxide (N₂O), which are representative organic and inorganic compounds emitted from semiconductor industries, were investigated. Three types of MPDs—a KPASS (MPD_K), [...] Read more.

In this study, the effects of various moisture pretreatment devices (MPDs) on the analytical process of trichloroethylene (TCE) and nitrous oxide (N₂O), which are representative organic and inorganic compounds emitted from semiconductor industries, were investigated. Three types of MPDs—a KPASS (MPD_K), a Nafion™ dryer (MPD_N), and a cooler (MPD_C)—were evaluated for their performance under sample gas conditions of 25 °C and 150 °C at various flow rates. MPD modification was also carried out to improve their performance at high loading capacities. The results indicated that humidity introduced significant bias in the measurement of TCE and N₂O according to the analyzers explored in this study. At a flow rate of 1 L/min, among the MPDs, MPD_N exhibited the highest moisture removal efficiency, followed by MPD_K and MPD_C. In terms of analyte recovery rates, MPD_K achieved the highest TCE recovery, followed by MPD_N and MPD_C, across all tested conditions. Conversely, MPD_C resulted in the lowest N₂O recovery rates, whereas MPD_K and MPD_N maintained over 95% recovery rates. At a flow rate of 4 L/min, MPD_N and MPD_C did not work at high temperatures. In contrast, the modified MPD_K, which received less investment compared to many other membranes, showed an acceptable moisture removal efficiency (>85%) and analyte recovery (>98%). Therefore, modified KPASS is recommended as a useful moisture pretreatment device for the analytical process of TCE and N₂O at both normal and high loading capacities. Full article

(This article belongs to the Section Air Pollution Control)

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14 pages, 3762 KiB

Open AccessArticle

Influence of Black Carbon on Measurement Errors in Scattering-Based Visibility Meters

by Zhihua Yang, Zefeng Zhang, Hengnan Guo and Jing Wang

Atmosphere 2025, 16(4), 467; https://doi.org/10.3390/atmos16040467 - 17 Apr 2025

Abstract

Visibility is a fundamental meteorological parameter critical for surface transportation, aviation, maritime navigation, and weather process investigation. Scattering visibility meters are extensively utilised for their simple design and rapid response; however, their measurement principle is inherently limited, as they only quantify the scattering [...] Read more.

Visibility is a fundamental meteorological parameter critical for surface transportation, aviation, maritime navigation, and weather process investigation. Scattering visibility meters are extensively utilised for their simple design and rapid response; however, their measurement principle is inherently limited, as they only quantify the scattering coefficient without assessing the absorption coefficient, potentially causing measurement errors. The World Meteorological Organisation (WMO) posits that the atmospheric absorption coefficient is usually relatively small and can be neglected, justifying the approximation of the extinction coefficient by the scattering coefficient. However, as black carbon is the predominant light-absorbing component in the atmosphere, an increase in its mass concentration markedly alters the atmospheric absorption coefficient, considerably impacting the accuracy of scattering-based visibility meters. Based on Mie scattering theory and incorporating both field observations and laboratory data, we systematically examined the effects of black carbon and its interactions with other aerosol components on the measurement errors of scattering visibility meters. Our findings revealed that the impact of black carbon on measurement errors is substantial, and under certain conditions, particularly pronounced. This influence is not only dependent on the mass concentration of black carbon but also closely associated with aerosol size distribution, mixing state, and the characteristics of other scattering aerosols. Due to the spatiotemporal variability of these factors, the impact of black carbon on visibility errors is uncertain. Therefore, during the calibration of scattering-based visibility meters, the effects of black carbon and its associated factors must be considered to enhance measurement accuracy. We propose calibration recommendations for scattering-based visibility meters aimed at reducing measurement errors and improving the accuracy of visibility assessments. Full article

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

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15 pages, 3455 KiB

Open AccessArticle

Spatiotemporal Dynamics of Retrogressive Thaw Slumps in the Shulenanshan Region of the Western Qilian Mountains

by Yu Zhou, Qingnan Zhang, Guoyu Li, Qingsong Du, Dun Chen, Junhao Chen, Anshuang Su, Miao Wang, Xu Wang and Benfeng Wang

Atmosphere 2025, 16(4), 466; https://doi.org/10.3390/atmos16040466 - 17 Apr 2025

Abstract

Climate warming is accelerating the degradation of permafrost, particularly in mid- to low-latitude regions, resulting in the widespread formation of thermokarst landscapes, including retrogressive thaw slumps (RTSs). These landforms, which are predominantly formed by the thawing of ice-rich permafrost, have been shown to [...] Read more.

Climate warming is accelerating the degradation of permafrost, particularly in mid- to low-latitude regions, resulting in the widespread formation of thermokarst landscapes, including retrogressive thaw slumps (RTSs). These landforms, which are predominantly formed by the thawing of ice-rich permafrost, have been shown to impact topography, hydrology, and ecosystem dynamics. However, spatiotemporal changes in RTS distribution and development in mid- to low-latitude permafrost regions are not well understood. This study investigates RTS spatiotemporal dynamics in the Heshenling area of the western Qilian Mountains using multi-temporal PlanetScope and Google Earth imagery, along with Sentinel-1 InSAR data acquired from 2014 to 2023. The results reveal 20 RTSs, averaging 3.7 ha in area, primarily distributed on slopes of 7–23° and at elevations of 3455–3651 m a.s.l. The deformation rates of RTSs ranged from −54 to 27 mm/year. Three developmental stages—active, stable, and mature—were identified through analysis of surface deformation and geometric variations. Active RTSs exhibited accelerated headscarp retreat and debris tongue expansion, with some slumps expanding by up to 35%. This study highlights high temperatures and rainfall as potential factors contributing to the accelerated development of RTS in arid alpine environments, and suggests that RTS activity is likely to accelerate with continued climate change. Full article

(This article belongs to the Special Issue Research About Permafrost–Atmosphere Interactions (2nd Edition))

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22 pages, 5718 KiB

Open AccessArticle

Drought Monitoring in the Agrotechnological Districts of the Semear Digital Center

by Tamires Lima da Silva, Luciana Alvim Santos Romani, Silvio Roberto Medeiros Evangelista, Mihai Datcu and Silvia Maria Fonseca Silveira Massruhá

Atmosphere 2025, 16(4), 465; https://doi.org/10.3390/atmos16040465 - 17 Apr 2025

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Abstract

Drought affects the agricultural sector, posing challenges for farm management, particularly among medium- and small-scale producers. This study uses climate data from remote sensing products to evaluate drought trends in the Semear Digital Center’s Agrotechnological Districts (DATs), which are characterized by a high [...] Read more.

Drought affects the agricultural sector, posing challenges for farm management, particularly among medium- and small-scale producers. This study uses climate data from remote sensing products to evaluate drought trends in the Semear Digital Center’s Agrotechnological Districts (DATs), which are characterized by a high concentration of small- and medium-sized farms in Brazil. Precipitation data from Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) and land surface temperature data from Moderate Resolution Imaging Spectroradiometer (MODIS) were applied to calculate the Standardized Precipitation–Evapotranspiration Index (SPEI) for a 6-month timescale from 2000 to 2024, with analysis divided into 2000–2012 and 2013–2024. Some limitations were noted: MODIS systematically underestimated temperatures, while CHIRPS tended to underestimate precipitation for most of the DATs. Despite discrepancies, these datasets remain valuable for drought monitoring in areas where long-term ground weather station data are lacking for SPEI assessments. Agricultural drought frequency and severity increased in the 2013–2024 period. Exceptional, extreme, severe, and moderate drought events rose by 7.3, 5.4, 2.2 and 1.0 times, respectively. These trends highlight the importance of adopting smart farming technologies to enhance the resilience of the DATs to climate change. Full article

(This article belongs to the Special Issue Observation of Climate Change and Cropland with Satellite Data)

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22 pages, 25259 KiB

Open AccessArticle

Spatial Modeling of Trace Element Concentrations in PM₁₀ Using Generalized Additive Models (GAMs)

by Mariacarmela Cusano, Alessandra Gaeta, Raffaele Morelli, Giorgio Cattani, Silvia Canepari, Lorenzo Massimi and Gianluca Leone

Atmosphere 2025, 16(4), 464; https://doi.org/10.3390/atmos16040464 - 16 Apr 2025

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Abstract

GAMs were implemented to evaluate the spatial variation in concentrations of 33 elements in PM₁₀, in their water-soluble and insoluble fractions used as tracers for different emission sources. Data were collected during monitoring campaigns (November 2016–February 2018) in the Terni basin [...] Read more.

GAMs were implemented to evaluate the spatial variation in concentrations of 33 elements in PM₁₀, in their water-soluble and insoluble fractions used as tracers for different emission sources. Data were collected during monitoring campaigns (November 2016–February 2018) in the Terni basin (an urban and industrial hotspot of Central Italy), using an innovative experimental approach based on high-spatial-resolution (23 sites, approximately 1 km apart) monthly samplings and the chemical characterization of PM₁₀. For each element, a model was developed using monthly mean concentrations as the response variable. As covariates, the temporal predictors included meteorological parameters (temperature, relative humidity, wind speed and direction, irradiance, precipitation, planet boundary layer height), while the spatial predictors encompassed distances from major sources, road length, building heights, land use variables, imperviousness, and population. A stepwise procedure was followed to determine the model with the optimal set of covariates. A leave-one-out cross-validation method was used to estimate the prediction error. Statistical indicators (Adjusted R-Squared, RMSE, FAC2, FB) were used to evaluate the performance of the GAMs. The spatial distribution of the fitted values of PM₁₀ and its elemental components, weighted over all sampling periods, was mapped at a resolution of 100 m. Full article

(This article belongs to the Section Air Quality)

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21 pages, 3848 KiB

Open AccessArticle

Variability and Trends of PM_2.5 Across Different Climatic Zones in Saudi Arabia: A Spatiotemporal Analysis

by Said Munir, Muhammad H. Siddiqui, Turki M. A. Habeebullah, Arjan O. Zamreeq, Norah E. Al-Zahrani, Alaa A. Khalil, M. Nazrul Islam, Abdalla A. Baligh, Muhammad Ismail and Saud Z. Al-Boqami

Atmosphere 2025, 16(4), 463; https://doi.org/10.3390/atmos16040463 - 16 Apr 2025

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Abstract

Atmospheric fine particles (PM_2.5) pose significant health risks by penetrating deep into the lungs and causing respiratory and cardiovascular issues. In Saudi Arabia, high PM_2.5 levels are driven by its geographic location and extreme climate. Therefore, analysis of PM_2.5 [...] Read more.

Atmospheric fine particles (PM_2.5) pose significant health risks by penetrating deep into the lungs and causing respiratory and cardiovascular issues. In Saudi Arabia, high PM_2.5 levels are driven by its geographic location and extreme climate. Therefore, analysis of PM_2.5 spatiotemporal variability is crucial for understanding its causes, impacts, and effective management. This study analyzed MERRA-2 reanalysis PM_2.5 data for 23 years (2001–2023). MERRA-2 data were validated with in situ observations in terms of several statistical metrics, including RMSE, FAC2, MAE, and Correlation Coefficient. The results revealed a significant spatial variation in PM_2.5 levels, with higher concentrations observed in the eastern and southeastern regions and lower concentrations observed in the western and northwestern regions, a trend confirmed by ground-level observations. Employing the robust Theil–Sen technique, temporal trends in PM_2.5 concentrations indicated an overall decreasing trend over the study period. At most sites, PM_2.5 levels increased until 2010 and then started decreasing, probably due to government interventions for reducing emissions, combating desertification, and enhancing tree plantations. Non-linear modeling provided a more accurate representation of complex trends compared to simple linear models. The findings underscore the need for continued national and regional efforts to mitigate PM_2.5 pollution by addressing its emission sources. Full article

(This article belongs to the Special Issue Global and Regional Perspectives on Particulate Matter and Air Quality: Environmental and Health Impacts, Challenges, Policies, and Solutions)

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22 pages, 89119 KiB

Open AccessArticle

Quantifying the Effects of Climate Change on the Urban Heat Island Intensity in Luxembourg—Sustainable Adaptation and Mitigation Strategies Through Urban Design

by Jürgen Junk, Céline Lett, Ivonne Trebs, Elke Hipler, Jairo A. Torres-Matallana, Ruben Lichti and Andreas Matzarakis

Atmosphere 2025, 16(4), 462; https://doi.org/10.3390/atmos16040462 - 16 Apr 2025

Viewed by 48

Abstract

Rapid urbanization and climate change intensify the urban heat island effect. This study quantifies the UHI impact in Luxembourg’s Pro-Sud region and explores sustainable mitigation strategies. In situ and mobile measurements, EURO-CORDEX regional climate projections (RCP4.5), and the FITNAH-3D urban climate model were [...] Read more.

Rapid urbanization and climate change intensify the urban heat island effect. This study quantifies the UHI impact in Luxembourg’s Pro-Sud region and explores sustainable mitigation strategies. In situ and mobile measurements, EURO-CORDEX regional climate projections (RCP4.5), and the FITNAH-3D urban climate model were used considering also future building developments. The results reveal a significant UHI effect, with substantial temperature and thermal stress level differences between urban and rural areas. Regional climate projections indicate a marked UHI intensification under future scenarios. FITNAH-3D simulations show increased thermal stress levels, especially in densely built areas, and highlight green infrastructure’s importance in mitigating UHI effects. Recommendations for spatial unit-specific urban climate measures specifically for vegetation, unsealing, and optimized urban design and planning are provided. Our research emphasizes the urgent need for tailored urban planning, adaptation, and mitigation strategies to enhance urban climate resilience and address thermal stress. Full article

(This article belongs to the Special Issue Urban Heat Islands and Global Warming (3rd Edition))

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21 pages, 6971 KiB

Open AccessArticle

Study on Dust Hazard Levels and Dust Suppression Technologies in Cabins of Typical Mining Equipment in Large Open-Pit Coal Mines in China

by Xiaoliang Jiao, Wei Zhou, Junpeng Zhu, Xinlu Zhao, Junlong Yan, Ruixin Wang, Yaning Li and Xiang Lu

Atmosphere 2025, 16(4), 461; https://doi.org/10.3390/atmos16040461 - 16 Apr 2025

Viewed by 64

Abstract

As the world’s largest open-pit coal producer, China faces severe dust pollution in mining operations. Cabins of mining equipment (electric shovels, haul trucks, drills) exhibit unique micro-environmental contamination due to dual-source pollution (external infiltration and internal secondary dust generation), posing severe health risks [...] Read more.

As the world’s largest open-pit coal producer, China faces severe dust pollution in mining operations. Cabins of mining equipment (electric shovels, haul trucks, drills) exhibit unique micro-environmental contamination due to dual-source pollution (external infiltration and internal secondary dust generation), posing severe health risks to miners. This study focused on electric shovel cabins at the Heidaigou open-pit coal mine to address cabin dust pollution. Through analysis of dust physicochemical properties, a pollution characteristic database was established. Field measurements and statistical methods revealed temporal–spatial variation patterns of dust concentrations, quantifying occupational exposure risks and providing theoretical foundations for dust control. A novel gradient-pressurized air purification system was developed for harsh mining conditions. Key findings include the following. (1) Both coal-shovel and rock-shovel operators were exposed to Level I (mild hazard level), with rock-shovel operators approaching Level II (moderate hazard level). (2) The system reduced respirable dust concentrations from 0.313 mg/m³ to 0.208 mg/m³ (≥33.34% improvement) in coal-shovel cabins and from 0.625 mg/m³ to 0.421 mg/m³ (≥32.64% improvement) in rock-shovel cabins. These findings offer vital guidance for optimizing cabin design, improving dust control, and developing scientific management strategies, thereby effectively protecting miners’ health and ensuring operational safety. Full article

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

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13 pages, 928 KiB

Open AccessArticle

Evaluating Soil Temperature Variations for Enhanced Radon Monitoring in Volcanic Regions

by Miroslaw Janik, Mashiro Hosoda, Shinji Tokonami, Yasutaka Omori and Naofumi Akata

Atmosphere 2025, 16(4), 460; https://doi.org/10.3390/atmos16040460 - 16 Apr 2025

Viewed by 38

Abstract

Soil temperature, a key factor in subsurface geochemical processes, is influenced by environmental and geological dynamics. This study analyzed hourly soil temperature variations at depths of 10 to 100 cm near the Sakurajima volcano, alongside concurrent ambient temperature measurements. By applying temperature models [...] Read more.

Soil temperature, a key factor in subsurface geochemical processes, is influenced by environmental and geological dynamics. This study analyzed hourly soil temperature variations at depths of 10 to 100 cm near the Sakurajima volcano, alongside concurrent ambient temperature measurements. By applying temperature models and statistical methods, we characterized both seasonal and short-term thermal dynamics, including soil-atmosphere thermal coupling. Our findings revealed a depth-dependent thermal diffusivity, establishing distinct thermal regimes within the soil profile. The soil’s strong thermal buffering capacity, evidenced by increasing amplitude attenuation and temporal lag with depth, allowed us to identify optimal instrument placement depths (80–100 cm) for minimal diurnal temperature influence. We also quantified the relationship between ambient temperature fluctuations and soil thermal response at various depths, as well as the impact of these temperature variations on soil permeability. These results enhance our understanding of subsurface thermal behaviour in volcanic environments and offer practical guidance for environmental monitoring and geohazard studies. Full article

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

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18 pages, 7773 KiB

Open AccessArticle

Expanding Lake Area on the Changtang Plateau Amidst Global Lake Water Storage Declines: An Exploration of Underlying Factors

by Da Zhi, Yang Pu, Chuan Jiang, Jiale Hu and Yujie Nie

Atmosphere 2025, 16(4), 459; https://doi.org/10.3390/atmos16040459 - 16 Apr 2025

Viewed by 57

Abstract

The remarkable expansion of lake areas across the Changtang Plateau (CTP, located in the central Tibetan Plateau) since the late 1990s has drawn considerable scientific interest, presenting a striking contrast to the global decline in natural lake water storage observed during the same [...] Read more.

The remarkable expansion of lake areas across the Changtang Plateau (CTP, located in the central Tibetan Plateau) since the late 1990s has drawn considerable scientific interest, presenting a striking contrast to the global decline in natural lake water storage observed during the same period. This study systematically investigates the mechanisms underlying lake area variations on the CTP by integrating glacierized area changes derived from the Google Earth Engine (GEE) platform with atmospheric circulation patterns from the ERA5 reanalysis dataset. Our analysis demonstrates that the limited glacier coverage on the CTP exerted significant influence only on glacial lakes in the southern region (r = −0.65, p < 0.05). The widespread lake expansion across the CTP predominantly stems from precipitation increases (r = 0.74, p < 0.01) associated with atmospheric circulation changes. Enhanced Indian summer monsoon (ISM) activity facilitates anomalous moisture transport from the Indian Ocean to the southwestern CTP, manifesting as increased specific humidity (Qa) in summer. Simultaneously, the weakened westerly jet stream reinforces moisture convergence across the CTP, driving enhanced annual precipitation. By coupling glacier coverage variations with atmospheric processes, this research establishes that precipitation anomalies rather than glacial meltwater primarily govern the extensive lake expansion on the CTP. These findings offer critical insights for guiding ecological security strategies and sustainable development initiatives on the CTP. Full article

(This article belongs to the Special Issue Advances in Methods for the Investigation of the Atmospheric Water Cycle)

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12 pages, 2013 KiB

Open AccessArticle

A New Approach to Estimating the Sensible Heat Flux in Bare Soils

by Francesc Castellví and Nurit Agam

Atmosphere 2025, 16(4), 458; https://doi.org/10.3390/atmos16040458 - 16 Apr 2025

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Abstract

The estimation of sensible heat flux (H) in drylands is important because it constitutes a significant portion of the net available surface energy. A model to estimate H half-hourly measurements for bare soils was derived by combining the surface renewal (SR) theory and [...] Read more.

The estimation of sensible heat flux (H) in drylands is important because it constitutes a significant portion of the net available surface energy. A model to estimate H half-hourly measurements for bare soils was derived by combining the surface renewal (SR) theory and the Monin–Obukhov similarity theory (MOST), involving the land surface temperature (LST), wind speed, and the air temperature in a period of half an hour, H_SR-LST. The surface roughness lengths for momentum (

z_{o m}

) and for heat (z_0h) were estimated at neutral conditions. The dataset included dry climates and different measurement heights (1.5 m up to 20 m). Root mean square error (

R M S E

) over the mean actual sensible heat flux estimate (H_EC), E

= \frac{R M S E}{\bar{H E C}} 100 %

, was considered excellent, good, and moderate for E values of up to 25%, 35%, and 40%, respectively. In stable conditions, H_SR-LST and H_MOST values were comparable and both were unacceptable (E > 40%). However, the RMSE using H_SR-LST ranged between 8 Wm² and 12 Wm² and performed slightly better than H_MOST. In unstable conditions, H_SR-LST was in excellent, good, and moderate agreement in 3, 6, and 5 cases, respectively; H_MOST was good in 3 cases; and the remaining 11 cases were intolerable because they required site-specific calibration. Full article

(This article belongs to the Special Issue The Water Cycle and Climate Change (3rd Edition))

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24 pages, 5064 KiB

Open AccessArticle

Predicting Ozone Concentrations in Ecologically Sensitive Coastal Zones Through Structure Mining and Machine Learning: A Case Study of Chongming Island, China

by Yan Liu, Tingting Hu, Yusen Duan and Jingqi Deng

Atmosphere 2025, 16(4), 457; https://doi.org/10.3390/atmos16040457 - 15 Apr 2025

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Abstract

Elevated O₃ concentrations pose a significant threat to human health and ecosystems, but little research has been performed on coastal wetlands near large cities. This study focuses on investigating the key factors affecting O₃ formation in the ecologically sensitive Dongtan Wetland [...] Read more.

Elevated O₃ concentrations pose a significant threat to human health and ecosystems, but little research has been performed on coastal wetlands near large cities. This study focuses on investigating the key factors affecting O₃ formation in the ecologically sensitive Dongtan Wetland (Chongming District, Shanghai, China) area. By comparing the performance of O₃ concentration prediction of multiple machine learning models, this study found that the random forest model achieved the highest accuracy (R² = 0.9, RMSE = 11.5). Feature importance and structure mining showed that peroxyacetyl nitrate (PAN), nitrogen oxides (NOx), temperature, wind direction, and relative humidity were the main drivers of O₃ formation. Specifically, PAN concentrations exceeding 0.1 ppb and temperatures above 3 °C were found to have a significant impact on O₃ levels, especially in spring, summer, and autumn. Trajectory analysis showed that westward urban pollution and emissions transported from the ocean were the main factors in O₃ formation in the area. This study highlights the need for targeted emission control strategies, especially for PAN precursors generated by ships and NOx generated by urban industries, providing important insights for improving air quality in ecologically sensitive coastal areas. Full article

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

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19 pages, 3327 KiB

Open AccessArticle

Southwest Pacific Tropical Cyclone Rapid Intensification Classification Utilizing Machine Learning

by Rupsa Bhowmick

Atmosphere 2025, 16(4), 456; https://doi.org/10.3390/atmos16040456 - 15 Apr 2025

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Abstract

This study evaluates the ability of three machine learning methods—decision tree classifier (DTC), random forest classifier (RFC), and XGBoost classifier (XGBC)—to classify and predict tropical cyclone (TC) rapid intensification (RI) and non-RI over the Southwest Pacific Ocean basin (SWPO) from 1982 to 2023. [...] Read more.

This study evaluates the ability of three machine learning methods—decision tree classifier (DTC), random forest classifier (RFC), and XGBoost classifier (XGBC)—to classify and predict tropical cyclone (TC) rapid intensification (RI) and non-RI over the Southwest Pacific Ocean basin (SWPO) from 1982 to 2023. Among the 324 TCs within the domain, 81 were identified as RI TCs, exhibiting a 24-h intensity increase of at least 15 ms⁻¹ at least once in their lifetime. Environmental variables used for the input matrix are extracted from the nearest grid cell corresponding to each RI and non-RI event’s geographic location and time of occurrence. Additionally, the geographic location of each event and its initial intensity positions (24-h prior) are also included in the model. The XGBC, with 10-fold cross-validation, became the optimum classifier by achieving the highest classification accuracy, as well as the lowest probability of false detection and the highest AUC score on the unseen data. The model identified the longitude of RI and non-RI events, initial intensity latitude, extent of initial intensity, and relative humidity at 850 hPa as the most important variables in the classification decision. This study will advance storm preparedness strategies for the SWPO nations through correctly predicting RI-TCs and prioritizing early prediction of contributing environmental variables. Full article

(This article belongs to the Section Climatology)

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21 pages, 4740 KiB

Open AccessArticle

An Assessment of the Impact of Climate Change on Maize Production in Northern Mexico

by Nuria Aide López Hernández, Aldo Rafael Martínez Sifuentes, Wiktor Halecki, Ramón Trucíos Caciano and Víctor Manuel Rodríguez Moreno

Atmosphere 2025, 16(4), 455; https://doi.org/10.3390/atmos16040455 - 15 Apr 2025

Viewed by 47

Abstract

Maize yield is highly sensitive to climate change and extreme weather events. In some locations, it is projected to decrease due to an increase in the average growing season temperature. The present study analyzes changes in temperature and precipitation extremes in the Comarca [...] Read more.

Maize yield is highly sensitive to climate change and extreme weather events. In some locations, it is projected to decrease due to an increase in the average growing season temperature. The present study analyzes changes in temperature and precipitation extremes in the Comarca Lagunera located in Northern Mexico, using the ETCCDI indices. We examined a 40-year period (1980–2020) using daily and monthly climate data provided by the National Meteorological Service. The climate databases were subjected to quality control, homogenization, and data filling using Climatol, and the ETCCDI indices were obtained using RClimDex software. Results indicate that the climate variable that most influences climate change in Comarca Lagunera is temperature, with increases in both maximum and minimum values. This situation is accentuating the drought in the Comarca Lagunera, which is supported by the increase in temperature-based indices. Furthermore, precipitation is the primary variable influencing the yield of rainfed maize, while maximum temperature affects the yield of irrigated maize. These results indicate that irrigation is functioning as a climate change adaptation strategy, reducing the impact of extreme weather on maize productivity, which could have a negative impact on water productivity in the study region in the short term. Full article

(This article belongs to the Section Biometeorology and Bioclimatology)

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27 pages, 2836 KiB

Open AccessArticle

A Multi-Model Approach to Pollen Season Estimations: Case Study for Olea and Quercus in Thessaloniki, Greece

by Sofia Papadogiannaki, Kostas Karatzas, Serafim Kontos, Anastasia Poupkou and Dimitrios Melas

Atmosphere 2025, 16(4), 454; https://doi.org/10.3390/atmos16040454 - 14 Apr 2025

Viewed by 41

Abstract

The accurate prediction of the Main Pollen Season (MPS) is crucial for public health and environmental management, particularly for allergenic and highly abundant taxa such as Olea and Quercus. This study presents a comparative evaluation of multiple predictive models for estimating MPS [...] Read more.

The accurate prediction of the Main Pollen Season (MPS) is crucial for public health and environmental management, particularly for allergenic and highly abundant taxa such as Olea and Quercus. This study presents a comparative evaluation of multiple predictive models for estimating MPS in Thessaloniki, Greece, from 2016 to 2022. The models examined include cumulative temperature-based approaches, Logistic Models (LM), the Distribution Method (DM), and Machine Learning Techniques (MLTs) such as Random Forest, Neural Networks, and Ensemble Learning. The results indicate that Double-Threshold temperature-based (DT) and LM models effectively capture the end of the pollen season, with differences from observed values ranging from 0 to 7 days. Meanwhile MLTs, particularly Random Forest, exhibit high accuracy in predicting its onset of the season, with deviations ranging from 0 to 10 days. Notably, the DT approach, which incorporates transition ranges, enhances the prediction reliability in complex urban environments. These findings contribute to the development of more robust aerobiological forecasting systems, supporting allergen exposure mitigation strategies and agricultural planning in Mediterranean climates. Future research should focus on multifold cross-validation techniques and advanced deep learning models, such as LSTMs (Long Short-Term Memory models), to further refine the prediction accuracy. These advancements would enable the development of more accurate and generalized forecasting models, contributing into a broader modeling system capable of predicting daily pollen concentrations, further supporting real-time pollen forecasting efforts. Full article

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

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18 pages, 2278 KiB

Open AccessArticle

Predicting the Impact of Global Climate Change on the Geographic Distribution of Anemochoric Species in Protected Areas

by Larissa Alves-de-Lima, Douglas Fernandes Rodrigues Alves, Diego Vinicius Anjos, Fernando Anco Valdivia and Helena Maura Torezan-Silingardi

Atmosphere 2025, 16(4), 453; https://doi.org/10.3390/atmos16040453 - 14 Apr 2025

Viewed by 101

Abstract

Protected areas are crucial sanctuaries for biodiversity, strictly prohibiting the direct exploitation of natural resources and helping to maintain viable populations and communities. However, even species within these areas face challenges from climate changes. This study compared the present distribution of five woody [...] Read more.

Protected areas are crucial sanctuaries for biodiversity, strictly prohibiting the direct exploitation of natural resources and helping to maintain viable populations and communities. However, even species within these areas face challenges from climate changes. This study compared the present distribution of five woody species (Aspidosperma tomentosum, Kielmeyera coriacea, Peixotoa tomentosa, Qualea multiflora, and Senna velutina) with their projected distribution (in 2080–2100) in 30 protected Brazilian national parks. Our objectives were to evaluate ecological niche models; determine which bioclimatic variables best explain the geographic distribution of species; assess the current distribution of these species; predict changes under distinct future climatic scenarios; and analyze the potential species richness within Brazilian national parks. We overlayed binarized maps of each species and extracted statistical metrics—mean potential, standard deviation, minimum, and maximum potential—using the ‘extract’ function (raster package, v.3.5-2) in the R platform. The results revealed the dynamic nature of species distribution, each one of them being affected by a specific group of factors. All species exhibited changes in their ecological niche or distribution areas in future projections, be it losing areas (A. tomentosum: 26.27–100%; K. coriacea: 38.39–100%; P. tomentosa: 40.46–96.66%; Q. multiflora: 7.34–100%; Senna velutina: 4.52–99.99%) or gaining areas (Q. multiflora: up to 92.21%, and S. velutina: up to 22.07%). We conclude that the potential species richness within Brazilian national parks will be lower in the future. This information is crucial for biodiversity conservation efforts, offering insights into species habitat dynamics and emphasizing the need for adaptive conservation strategies. This study reinforces the urgency of preserving natural ecosystems to ensure a sustainable future for their flora and fauna. Full article

(This article belongs to the Special Issue Vegetation and Climate Relationships (3rd Edition))

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15 pages, 2513 KiB

Open AccessArticle

Analysis of Flux Contribution Area in a Peatland of the Permafrost Zone in the Greater Khingan Mountains

by Jizhe Lian, Li Sun, Yongsi Wang, Xianwei Wang and Yu Du

Atmosphere 2025, 16(4), 452; https://doi.org/10.3390/atmos16040452 - 14 Apr 2025

Viewed by 62

Abstract

Flux contribution area analysis is a valuable method for identifying greenhouse gas flux sources and their spatiotemporal variations. Flux footprint models are commonly applied to determine the origin of flux observations and estimate the location, size, and relative contributions of different flux source [...] Read more.

Flux contribution area analysis is a valuable method for identifying greenhouse gas flux sources and their spatiotemporal variations. Flux footprint models are commonly applied to determine the origin of flux observations and estimate the location, size, and relative contributions of different flux source regions. Based on eddy covariance observation data, this study utilized the Kljun model and ART Footprint Tool to analyze the source area dynamics of peatland CO₂ fluxes in the permafrost region of the Greater Khingan Mountains, examining the distribution characteristics of flux contribution areas across different seasons, and atmospheric conditions, while also assessing the influence of vegetation types on these areas. The results indicated that: (1) due to regional climate conditions and terrain, the predominant wind direction in all seasons was northeast-southwest, aligning with the main flux contribution direction; (2) when the flux contribution area reached 90%, the maximum source area distances under the stable and unstable atmospheric conditions were 393.3 and 185.6 m, respectively, with the range and distance of flux contribution areas being significantly larger under stable conditions; and (3) the peatland vegetation primarily consisted of trees, tall shrubs, dwarf shrubs, sedges, and mosses, among which shrub communities dominating flux contribution areas (55.6–59.1%) contribute the most to the flux contribution areas, followed by sedges (16.7–17.7%) and mosses (18.6–19.9%), while the influence of trees (0.4–0.6%) was minimal. Full article

(This article belongs to the Special Issue Research About Permafrost–Atmosphere Interactions (2nd Edition))

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23 pages, 3901 KiB

Open AccessArticle

Relative Contributions from Wind, Storm Surge, and Inland Flooding to Tropical Cyclone Damage from 1925 to 2000 in North Carolina

by Douglas Hilderbrand and Lian Xie

Atmosphere 2025, 16(4), 451; https://doi.org/10.3390/atmos16040451 - 13 Apr 2025

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Abstract

This study analyzes the relative contributions from wind, storm surge, and inland flooding to tropical cyclone damage from 1925 to 2000 in North Carolina. It emphasizes the importance of regional tropical cyclone risk assessments, using North Carolina as a case study. A revised [...] Read more.

This study analyzes the relative contributions from wind, storm surge, and inland flooding to tropical cyclone damage from 1925 to 2000 in North Carolina. It emphasizes the importance of regional tropical cyclone risk assessments, using North Carolina as a case study. A revised normalization method, incorporating housing data instead of population data, revealed more accurate property damage estimations. From 1940 to 2000, housing in coastal North Carolina grew by 780%, compared to a 370% population increase. Using this method, combined damages from 1954 to 1955 tropical storms would exceed USD 18 billion in year 2000 values, compared to USD 13 billion during 1996–1999. Flooding accounted for 40% of the tropical cyclone damage in North Carolina during the study period, exceeding national averages, with wind and storm surge contributing 35% and 25%, respectively. Rainfall analysis showed a weaker link to cyclone intensity. The catastrophic flooding from Hurricane Floyd in 1999 deposited approximately 17 km³ of water, surpassing roughly 13 km³ from Hurricane Fran (1996). While major hurricanes caused 83% of hurricane damage nationally during the study period, they contributed about 70% in North Carolina, with category-2 hurricanes adding 21.4%. These findings highlight the need to consider weaker cyclones, especially category-2 storms, in North Carolina regional hurricane risk management. Full article

(This article belongs to the Section Meteorology)

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12 pages, 2533 KiB

Open AccessArticle

Revealing Vertical Distribution of Atmospheric Mercury Using Drone-Based Monitoring Technique: A Case Study in Vietnam

by Duc Thanh Nguyen, Kiet Le Nguyen Tan, Hien Bich Vo, Pham Thi Dieu Huong, Nguyen Thi Thuy, Le Quoc Hau and Ly Sy Phu Nguyen

Atmosphere 2025, 16(4), 450; https://doi.org/10.3390/atmos16040450 - 13 Apr 2025

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Abstract

Unmanned aerial vehicles (UAVs) have emerged as effective tools for monitoring air pollution across varying altitudes, including assessing atmospheric mercury (Hg) levels. However, studies on the vertical distribution of atmospheric Hg (i.e., total gaseous mercury–TGM) concentrations remain limited, particularly in Southeast Asia. This [...] Read more.

Unmanned aerial vehicles (UAVs) have emerged as effective tools for monitoring air pollution across varying altitudes, including assessing atmospheric mercury (Hg) levels. However, studies on the vertical distribution of atmospheric Hg (i.e., total gaseous mercury–TGM) concentrations remain limited, particularly in Southeast Asia. This study utilized a UAV equipped with a TGM sampling device to measure concentrations at different altitudes in Ben Cat City, an industrial area in Southern Vietnam. The purpose of this study is to examine the applicability of UAV in investigating the altitudinal distribution of TGM and to analyze specific case studies related to Hg emissions from stack. A total of 36 flight experiments were conducted (including 36 concurrently ground level measurements), including 50 m (20 flights), 200 m (7 flights), and 500 m (9 flights). TGM concentrations increase noticeably with altitude under stack emission conditions, while they remain relatively consistent at all altitudes during non-emission conditions. Under the emission conditions, three vertical distribution patterns were observed: (1) elevated TGM concentrations at higher altitudes compared to ground level; (2) lower TGM concentrations at higher altitudes relative to ground level; and (3) nearly equivalent TGM concentrations between ground level and higher altitudes, with differences less than 0.4 ng m⁻³. The observed distributions imply the important role of atmospheric dynamics in understanding the dispersion of pollutants and the impact of emissions. This study pioneers the use of UAVs in Vietnam for simultaneous TGM measurements across altitudes, highlights their potential for atmospheric Hg monitoring, and improves stack emission management. Full article

(This article belongs to the Special Issue Cycles and Transformations of Mercury in the Atmosphere and Ecosystems)

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11 pages, 3382 KiB

Open AccessArticle

High-Resolution Analysis of Temporal Variation and Driving Factors of CO₂ Concentration in Nanning City in Spring 2024

by Jinghang Feng, Xuemei Chen, Huilin Liu, Zhaoyu Mo, Shiyang Yan, Xiaoyu Peng, Hongjiao Li, Hao Li, Hui Liao and Jiahui Lu

Atmosphere 2025, 16(4), 449; https://doi.org/10.3390/atmos16040449 - 12 Apr 2025

Viewed by 73

Abstract

In this study, based on high-resolution online monitoring data of CO₂ concentration in Nanning City in the spring of 2024, we analyzed the characteristics of diurnal and monthly changes of CO₂ concentration in Nanning City and explored the influencing factors through [...] Read more.

In this study, based on high-resolution online monitoring data of CO₂ concentration in Nanning City in the spring of 2024, we analyzed the characteristics of diurnal and monthly changes of CO₂ concentration in Nanning City and explored the influencing factors through the background sieving method and Lagrangian Particle Dispersion Model (LPDM) traceability simulations combined with meteorological factor analysis. The results demonstrates that the diurnal variation of CO₂ concentration in Nanning City exhibits a bimodal pattern of peak in the afternoon and trough in the early morning, with a mean concentration of 460 ± 15 ppm. Transportation emissions were identified as the dominant source of this variation. The trend of monthly concentration changes was first increasing and then decreasing, with an increase in February–March and a decrease in April, indicating that it was affected by the combined effect of vegetation photosynthesis and urban human activities. The results of the background sieving method and traceability simulation analysis showed that the CO₂ concentration in Nanning City was more affected by local emission sources than sinks, and the industrial sources and transportation sources in the north–south direction had a significant effect on the CO₂ concentration. This research provides critical data support for formulating carbon reduction strategies and coordinated atmospheric environment management in subtropical cities. Full article

(This article belongs to the Section Air Pollution Control)

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19 pages, 2732 KiB

Open AccessArticle

Efficacy of Ventilation Air Purifiers in Improving Classroom Air Quality: A Case Study in South Korea

by Jae Jung Lee and Soontae Kim

Atmosphere 2025, 16(4), 448; https://doi.org/10.3390/atmos16040448 - 11 Apr 2025

Viewed by 84

Abstract

Indoor air quality (IAQ) in schools significantly affects health and academic performance; however, effective interventions for poor air quality remain limited, particularly in settings with restricted natural ventilation. This study evaluated the effectiveness of ventilation-type air purifiers in improving classroom IAQ in a [...] Read more.

Indoor air quality (IAQ) in schools significantly affects health and academic performance; however, effective interventions for poor air quality remain limited, particularly in settings with restricted natural ventilation. This study evaluated the effectiveness of ventilation-type air purifiers in improving classroom IAQ in a South Korean elementary school. PM₁₀, PM_2.5, and CO₂ concentrations were monitored over 18 days (14–31 May 2021) in two classrooms—one equipped with a ventilation-type air purifier and the other serving as a control. In the classroom with the air purifier, daily average concentrations of PM₁₀, PM_2.5, and CO₂ decreased by 23.7%, 22.8%, and 21.1%, respectively, from baseline levels. The air purifier effectively reduced pollutant infiltration during periods of severe outdoor air pollution and stabilized pollutant levels during active class hours. Its efficacy was particularly prominent under conditions of restricted natural ventilation, high indoor activity, and fluctuating outdoor pollution levels. IAQ varied significantly between weekdays and weekends; pollutant levels were higher on weekdays due to occupancy and classroom activities, whereas weekends exhibited reduced concentrations. These findings suggest that ventilation-type air purifiers provide a viable strategy for improving IAQ in schools with limited ventilation. Future research should examine their long-term performance across different seasons and architectural settings. Full article

(This article belongs to the Section Air Quality)

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17 pages, 4307 KiB

Open AccessArticle

Research on Lightning Prediction Based on GCN-LSTM Model Integrating Spatiotemporal Features

by Wei Zhou, Wenqiang Wang and Xupeng Wang

Atmosphere 2025, 16(4), 447; https://doi.org/10.3390/atmos16040447 - 11 Apr 2025

Viewed by 84

Abstract

To overcome the limitations of spatiotemporal feature extraction that are inherent in conventional lightning warning algorithms relying solely on temporal analysis, we propose a novel prediction framework integrating a Graph Convolutional Network (GCN), Long Short-Term Memory (LSTM) architecture, and a multi-head attention mechanism. [...] Read more.

To overcome the limitations of spatiotemporal feature extraction that are inherent in conventional lightning warning algorithms relying solely on temporal analysis, we propose a novel prediction framework integrating a Graph Convolutional Network (GCN), Long Short-Term Memory (LSTM) architecture, and a multi-head attention mechanism. The methodology innovatively constructs station adjacency matrices based on geographical distances between meteorological monitoring stations in Qingdao, Shandong Province, China, where GCN layers capture inter-station spatial dependencies while LSTM units extract localized temporal dynamics. A dedicated multi-head attention module was developed to enable adaptive fusion of global spatiotemporal patterns, significantly enhancing lightning warning level prediction accuracy at target locations. The GCN-LSTM model achieved 93% accuracy, 59% precision, 64% recall, and a 59% F1 score. Experimental evaluation on operational meteorological data demonstrated the model’s superior performance: it achieved statistically significant accuracy improvements of 6% (p = 0.019), 3% (p = 0.026), and 2% (p = 0.03) over conventional LSTM, TGCN, and CNN-RNN baselines, respectively. Comprehensive assessments through precision–recall analysis, confusion matrix decomposition, and spatial generalizability tests confirmed the framework’s robustness. The key theoretical advancement introduced by this study lies in the synergistic coupling of graph-based spatial modeling with deep temporal sequence learning, augmented by attention-driven feature fusion—an architectural innovation addressing critical gaps in existing single-modality approaches. This methodology establishes a new paradigm for extreme weather prediction with direct applications in lightning hazard mitigation. Full article

(This article belongs to the Section Meteorology)

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26 pages, 18027 KiB

Open AccessArticle

Spatiotemporal Evolution of Regional Air Pollution Exposure and Health Effects Assessment in Jiangsu Province, China

by Jin Yang, Qiuyu Ju, Shifan Chen, Chen Xu and Yang Cao

Atmosphere 2025, 16(4), 446; https://doi.org/10.3390/atmos16040446 - 11 Apr 2025

Viewed by 120

Abstract

China’s industrialization and urbanization processes have recently accelerated, leading to the rapid expansion of urban built-up areas. Fossil fuels, such as natural gas, oil, and coal, are consumed in large quantities, resulting in the accumulation of atmospheric pollutants. Severe PM2.5 and O₃ [...] Read more.

China’s industrialization and urbanization processes have recently accelerated, leading to the rapid expansion of urban built-up areas. Fossil fuels, such as natural gas, oil, and coal, are consumed in large quantities, resulting in the accumulation of atmospheric pollutants. Severe PM2.5 and O₃ pollution poses significant human health risks, including respiratory diseases, cardiovascular and cerebrovascular diseases, and lung cancer. This study utilized data from various observation stations in Jiangsu Province, the annual statistical yearbook data, and statistical data, such as baseline mortality and socioeconomic indicators, to quantitatively analyze the concentration characteristics of PM2.5 and O₃, premature deaths related to pollutant exposure, and negative health effects in Jiangsu Province from 2018 to 2023. The study examined the spatiotemporal evolution patterns of pollutant concentrations, related premature deaths, and negative health effects in various cities within Jiangsu Province under policy-driven conditions. The results show that (1) the annual average concentration of PM2.5 in Jiangsu Province decreased from 105.88 μg/m³ in 2018 to 55.04 μg/m³ in 2023, marking a reduction of 48.01%. (2) The total number of premature deaths due to long-term exposure to PM2.5 decreased by approximately 87%, whereas the total number of premature deaths due to long-term exposure to O₃ increased by approximately 216%. (3) City level (2.377**) and population structure (1.068**) play an important role in the health effects of air pollution. (4) Short-term exposure to high concentrations of pollutants has a significant negative impact on the health of individuals with underlying diseases. Full article

(This article belongs to the Special Issue Air Pollution in China (3rd Edition))

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