Journal Description
Atmosphere
Atmosphere
is an international, peer-reviewed, open access journal of scientific studies related to the atmosphere published monthly online by MDPI. The Italian Aerosol Society (IAS) and Working Group of Air Quality in European Citizen Science Association (ECSA) are affiliated with Atmosphere and their members receive a discount on the article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), Ei Compendex, GEOBASE, GeoRef, Inspec, CAPlus / SciFinder, Astrophysics Data System, and other databases.
- Journal Rank: CiteScore - Q2 (Environmental Science (miscellaneous))
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 16.1 days after submission; acceptance to publication is undertaken in 2.8 days (median values for papers published in this journal in the second half of 2024).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Testimonials: See what our editors and authors say about the Atmosphere.
- Companion journals for Atmosphere include: Meteorology and Aerobiology.
Impact Factor:
2.5 (2023);
5-Year Impact Factor:
2.6 (2023)
Latest Articles
Assessment of Coastal Zone Vulnerability in Context of Sea-Level Rise and Inundation Risk in Qatar
Atmosphere 2025, 16(5), 622; https://doi.org/10.3390/atmos16050622 (registering DOI) - 19 May 2025
Abstract
Coastal zones represent the most active interfaces where natural processes and human activities converge, making them crucial for biodiversity and socioeconomic development. These zones are characterized by their fragility and susceptibility to frequent natural disasters, such as floods and erosion, which are exacerbated
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Coastal zones represent the most active interfaces where natural processes and human activities converge, making them crucial for biodiversity and socioeconomic development. These zones are characterized by their fragility and susceptibility to frequent natural disasters, such as floods and erosion, which are exacerbated by high-intensity human activities and urban expansion. The ongoing challenges posed by rising sea levels and climate change necessitate robust scientific assessments of coastal vulnerability to ensure effective disaster prevention and environmental protection. This paper introduces a comprehensive evaluation system for assessing coastal zone vulnerability, utilizing multi-source data to address ecological vulnerabilities stemming from sea-level rise and climate change impacts. This system is applied to examine the specific case of Qatar, where rapid urban development and a high population density in coastal areas heighten the risk of flooding and inundation. Employing remote sensing data and Geographic Information Systems (GISs), this research leverages spatial interpolation techniques and high-resolution digital elevation models (DEMs) to identify and evaluate high-risk zones susceptible to sea-level rise. In this study, the hydrological connectivity model, bathtub technique, and CVI are interconnected tools that complement each other to assess future flooding risks under various climate change projections, highlighting the increased probability of coastal hazards. The findings underscore the urgent need for adaptive planning and regulatory frameworks to mitigate these risks, providing technical support for the sustainable development of coastal communities globally and in Qatar. This approach not only informs policy makers, but also aids in the strategic planning required to foster resilient coastal infrastructure capable of withstanding both current and future environmental challenges.
Full article
(This article belongs to the Special Issue Climate Change and Regional Sustainability in Arid Lands (2nd Edition))
Open AccessArticle
Estimation of High Spatial Resolution CO2 Concentration in China from 2010 to 2022 Based on Multi-Source Carbon Satellite Data
by
Shanzhao Cai, Heng Dong, Bo Zhang and Huan Huang
Atmosphere 2025, 16(5), 621; https://doi.org/10.3390/atmos16050621 (registering DOI) - 19 May 2025
Abstract
The increase in the carbon dioxide (CO2) concentration is a major driver of global warming, presenting significant challenges to ecosystems and human societies. Satellite remote sensing technology can monitor the continuous spatial variation of the atmospheric CO2 column concentration (XCO
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The increase in the carbon dioxide (CO2) concentration is a major driver of global warming, presenting significant challenges to ecosystems and human societies. Satellite remote sensing technology can monitor the continuous spatial variation of the atmospheric CO2 column concentration (XCO2), but its global application is limited by the narrow observational swath. To address this, this study effectively integrates XCO2 data retrieved from the GOSAT and OCO-2 satellites using atmospheric profile adjustment and spatial grid integration techniques. Based on this, a multi-machine learning ensemble algorithm (MLE) was developed, which successfully estimated the spatially continuous XCO2 concentration in China from 2010 to 2022 (ChinaXCO2-MLE). The results indicate that, compared to individual satellite observations, the integration of multi-source satellite XCO2 data significantly improves the spatiotemporal coverage. The overall R2 of the MLE model was 0.97, with an RMSE of 0.87 ppmv, outperforming single machine learning models. The ChinaXCO2-MLE shows good consistency with the observational records from two background stations in China, with R2 values of 0.93 and 0.78, and corresponding RMSEs of 1.00 ppmv and 1.32 ppmv. This study also reveals the seasonal and regional variations in China’s XCO2 concentration: the highest concentration occurs in spring, the lowest concentration occurs in northern regions during summer, and the lowest concentration occurs in southern regions during autumn. From 2010 to 2022, the XCO2 concentration continued to rise, but the growth rate has slowed due to the implementation of air pollution prevention and energy conservation policies. The spatially continuous XCO2 data provide a more comprehensive understanding of carbon variation and offer a valuable reference for achieving China’s carbon neutrality goals.
Full article
(This article belongs to the Section Air Quality)
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Open AccessArticle
Investigating the Sensitivity of Modelled Ozone Levels in the Mediterranean to Dry Deposition Parameters
by
André Barreirinha, Sabine Banzhaf, Markus Thürkow, Carla Gama, Michael Russo, Enrico Dammers, Martijn Schaap and Alexandra Monteiro
Atmosphere 2025, 16(5), 620; https://doi.org/10.3390/atmos16050620 (registering DOI) - 19 May 2025
Abstract
The exposure to elevated levels of ozone contributes to respiratory diseases and ecosystem degradation. Mediterranean countries are among those most affected by high ozone concentrations, which are generally overestimated by chemistry transport models underscoring the importance of improving the accuracy of air quality
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The exposure to elevated levels of ozone contributes to respiratory diseases and ecosystem degradation. Mediterranean countries are among those most affected by high ozone concentrations, which are generally overestimated by chemistry transport models underscoring the importance of improving the accuracy of air quality modelling. This study introduces an enhanced Mediterranean dry deposition description within the LOTOS-EUROS model framework, focusing on refining key vegetation parameters for the Mediterranean climate zone, with the goal to better estimate deposition and connected concentration values. Adjustments were made to the vegetation type dependent Jarvis functions for temperature and vapour pressure deficit, as well as to the maximum stomatal conductance across four land use types: arable land, crops, deciduous broadleaf forest, and coniferous evergreen forest. The model’s baseline run showed a widespread overestimation of ozone. Adjustments to the dry deposition routines reduced this overestimation, but the model simulation incorporating all changes still showed elevated ozone levels. Both runs displayed moderate spatial correlation with observations from 117 rural background monitoring stations, and most stations exhibited a temporal correlation between 0.5 and 0.8. An improved RMSE and bias were noted at the majority of the stations (114 out of 117) for the model simulation incorporating all changes. The monthly analysis indicated consistent overestimation at two Portuguese sites beginning in March. The model effectively tracked temporal changes overall. However, the diurnal analysis revealed site-specific differences: an overestimation at the station closest to highly populated areas at night, while rural stations aligned better with observed values. These results highlight the benefits of region-specific model adaptations and lay the groundwork for further advancements, such as incorporating detailed vegetation classifications and seasonal variations.
Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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Open AccessArticle
Characteristic Analysis of the Extreme Precipitation over South China During the Dragon-Boat Precipitation in 2022
by
Meixia Chen, Yufeng Xue, Juliao Qiu, Chunlei Liu, Shuqin Zhang, Jianjun Xu and Ziye Zhu
Atmosphere 2025, 16(5), 619; https://doi.org/10.3390/atmos16050619 (registering DOI) - 19 May 2025
Abstract
Using multi-source precipitation datasets including NASA GPM (IMERG), GPCP, ECMWF ERA5, and station precipitation data from the China Meteorological Administration (CMA), along with ERA5 reanalysis fields for atmospheric circulation analysis, this study investigates the extreme precipitation events during the “Dragon-Boat Precipitation” period from
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Using multi-source precipitation datasets including NASA GPM (IMERG), GPCP, ECMWF ERA5, and station precipitation data from the China Meteorological Administration (CMA), along with ERA5 reanalysis fields for atmospheric circulation analysis, this study investigates the extreme precipitation events during the “Dragon-Boat Precipitation” period from 20 May to 21 June over South China in 2022 using the synoptic diagnostic method. The results indicate that the total precipitation during this period significantly exceeded the climatological average, with multiple large-scale extreme rainfall events characterized by high intensity, extensive coverage, and prolonged duration. The spatial distribution of precipitation exhibited a north-more-south-less pattern, with the maximum rainfall center located in the Nanling Mountains, particularly in the Shaoguan–Qingyuan–Heyuan region of Guangdong Province, where peak precipitation exceeded 1100 mm, and the mean precipitation was approximately 1.7 times the climatology from the GPM data. The average daily precipitation throughout the period was 17.5 mm/day, which was 6 mm/day higher than the climatological mean, while the heaviest rainfall on 13 June reached 39 mm/day above the average, exceeding two standard deviations. The extreme precipitation during the “Dragon-Boat Precipitation” period in 2022 was associated with an anomalous deep East Asian trough, an intensified South Asian High, a stronger-than-usual Western Pacific Subtropical High, an enhanced South Asian monsoon and South China Sea monsoon, and the dominance of a strong Southwesterly Low-Level Jet (SLLJ) over South China. Two major moisture transport pathways were established: one from the Bay of Bengal to South China and another from the South China Sea, with the latter contributing a little higher amount of water vapor transport than the former. The widespread extreme precipitation on 13 June 2022 was triggered by the anomalous atmospheric circulation conditions. In the upper levels, South China was located at the northwestern periphery of the slightly stronger-than-normal Western Pacific Subtropical High, intersecting with the base of a deep trough associated with an anomalous intense Northeast China Cold Vortex (NCCV). At lower levels, the region was positioned along a shear line formed by anomalous southwesterly and northerly winds, where exceptionally strong southwesterly moisture transport, significant moisture convergence, and intense vertical updraft led to the widespread extreme rainfall event on that day.
Full article
(This article belongs to the Special Issue Climate Change and Extreme Weather Disaster Risks (2nd Edition))
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Open AccessArticle
The Risk of Developing Tinnitus and Air Pollution Exposure
by
Po-Yu Lai, Chang-Yin Lee, Kuang-Hsi Chang, Yu-Kang Chang, Yi-Chao Hsu, Ing-Ming Chiu, Stella Chin-Shaw Tsai, Der-Yang Cho, Cheng-Li Lin, Tsung-Hsing Lin and Wu-Lung Chuang
Atmosphere 2025, 16(5), 618; https://doi.org/10.3390/atmos16050618 (registering DOI) - 19 May 2025
Abstract
(1) Background: The role of air pollutants as risk factors for tinnitus remains unclear. To address this gap in research, we conducted a nationwide retrospective cohort study in Taiwan by integrating patients’ clinical data with daily air quality data to evaluate the environmental
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(1) Background: The role of air pollutants as risk factors for tinnitus remains unclear. To address this gap in research, we conducted a nationwide retrospective cohort study in Taiwan by integrating patients’ clinical data with daily air quality data to evaluate the environmental risk factors associated with tinnitus. (2) Methods: The Taiwan National Health Research Database (NHIRD) includes medical records for nearly all residents of Taiwan. To assess pollution levels, we used daily air quality data from the Taiwan Environmental Protection Agency regarding SO2, CO, NO, NOX, and particulate matter (PM2.5 and PM10). We merged the NHIRD data with air quality information based on the residents’ locations and the positions of air quality monitoring stations. Pollutant levels were then categorized into quartiles (Q1, Q2, Q3, and Q4). (3) Results: This study included 284,318 subjects. After controlling for covariates, the adjusted HR (95 CI%) for tinnitus increased with increasing SO2, CO, NO, NOX, PM2.5, and PM10 exposure levels, specifically from 1.24 (95 CI% = 1.18, 1.30) to 1.35 (95 CI% = 1.28–1.41); from 1.15 (95 CI% = 1.09, 1.21) to 1.90 (95 CI% = 1.81, 2.00); from 0.86 (95 CI% = 0.82, 0.91) to 1.69 (95 CI% = 1.62, 1.77); from 1.62 (95 CI% = 1.54, 1.71) to 1.69 (95 CI% = 1.60, 1.77); from 0.16 (95 CI% = 0.15, 0.18) to 2.70 (95 CI% = 2.57, 2.84); and from 2.53 (95 CI% = 2.38, 2.69) to 3.58 (95 CI% = 3.39, 3.78), respectively, compared to the Q1 concentrations for all air pollutants. (4) Conclusions: During the 15-year follow-up period, we found a significant positive correlation between air pollutant exposure and the risk of tinnitus.
Full article
(This article belongs to the Special Issue Air Pollution Exposure and Health Impact Assessment (3rd Edition))
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Open AccessArticle
Research on the Mechanism of the Impact of Green View Index of Urban Streets on Thermal Environment: A Machine Learning-Driven Empirical Study in Hangzhou, China
by
Qiguan Wang, Yanjun Hu and Hai Yan
Atmosphere 2025, 16(5), 617; https://doi.org/10.3390/atmos16050617 (registering DOI) - 19 May 2025
Abstract
This study investigates the relationship between Green View Index (GVI) and street thermal environment in Hangzhou’s main urban area during summer, quantifying urban greenery’s impact on diurnal/nocturnal thermal conditions to inform urban heat island mitigation strategies. Multi-source data (3D morphological metrics, LCZ classifications,
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This study investigates the relationship between Green View Index (GVI) and street thermal environment in Hangzhou’s main urban area during summer, quantifying urban greenery’s impact on diurnal/nocturnal thermal conditions to inform urban heat island mitigation strategies. Multi-source data (3D morphological metrics, LCZ classifications, mobile measurements) were integrated with deep learning-derived street-level GVI through image analysis. A random forest-multiple regression hybrid model evaluated spatiotemporal variations and GVI impacts across time, street orientation, and urban-rural gradients. Key findings include: (1) Urban street Ta prediction model: Daytime model: R2 = 0.54, RMSE = 0.33 °C; Nighttime model: R2 = 0.71, RMSE = 0.42 °C. (2) GVI shows significant inverse association with temperature, A 0.1 unit increase in GVI reduced temperatures by 0.124°C during the day and 0.020 °C at night. (3) Orientation effects: North–south streets exhibit strongest cooling (1.85 °C daytime reduction), followed by east–west; northeast–southwest layouts show negligible impact; (4) Canyon geometry: Low-aspect canyons (H/W < 1) enhance cooling efficiency, while high-aspect canyons (H/W > 2) retain nocturnal heat despite daytime cooling; (5) Urban-rural gradient: Cooling peaks in urban-fringe zones (10–15 km daytime, 15–20 km nighttime), contrasting with persistent nocturnal warmth in urban cores (0–5 km); (6) LCZ variability: Daytime cooling intensity peaks in LCZ3, nighttime in LCZ6. These findings offer scientific evidence and empirical support for urban thermal environment optimization strategies in urban planning and landscape design. We recommend dynamic coupling of street orientation, three-dimensional morphological characteristics, and vegetation configuration parameters to formulate differentiated thermal environment design guidelines, enabling precise alignment between mitigation measures and spatial context-specific features.
Full article
(This article belongs to the Section Biometeorology and Bioclimatology)
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Open AccessArticle
Homogenization of the Probability Distribution of Climatic Time Series: A Novel Algorithm
by
Peter Domonkos
Atmosphere 2025, 16(5), 616; https://doi.org/10.3390/atmos16050616 (registering DOI) - 18 May 2025
Abstract
The aim of the homogenization of climatic time series is to remove non-climatic biases from the observed data, which are caused by technical or environmental changes during the period of observations. This bias removal is generally more successful for long-term trends and annual
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The aim of the homogenization of climatic time series is to remove non-climatic biases from the observed data, which are caused by technical or environmental changes during the period of observations. This bias removal is generally more successful for long-term trends and annual means than for monthly and daily values. The homogenization of probability distribution (HPD) may improve data accuracy even for daily data when the signal-to-noise ratio favors its application. HPD can be performed by quantile matching or spatial interpolations, but both of them have drawbacks. This study presents a new algorithm which helps to increase homogenization accuracy in all temporal and spatial scales. The new method is similar to quantile matching, but section mean values of the probability distribution function (PDF) are compared instead of individual daily values. The input dataset of the algorithm is identical with the homogenization results for section means of the studied time series. The algorithm decides about statistical significance for each break detected during the homogenization of the section means, and skips the insignificant breaks. Correction terms for removing the inhomogeneity biases of PDF are calculated jointly by a Benova-like equation system, a low pass filter is used for smoothing the prime results, and the mean value of the input time series between two consecutive detected breaks is preserved for each of such sections. This initial version does not deal with seasonal variations either during HPD or in other steps of the homogenization. The method has been tested connecting HPD to ACMANTv5.3, and using overall 8 wind speed and relative humidity datasets of the benchmark of European project INDECIS. The results show 4 to 12 percent RMSE reduction by HPD in all temporal scales, except for the extreme tails where a part of the results are weaker.
Full article
(This article belongs to the Special Issue Data Analysis in Atmospheric Research)
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Open AccessArticle
A Forgotten Aurora: Revisiting the 19 March 1950 Aurora Australis Through Historical Records
by
Víctor M. S. Carrasco and José M. Vaquero
Atmosphere 2025, 16(5), 615; https://doi.org/10.3390/atmos16050615 (registering DOI) - 18 May 2025
Abstract
This study investigates the aurora australis event of 19 March 1950, which was reported across multiple locations in Australia, including Hobart, Sydney, and as far north as Goondiwindi. Despite its significance as a historical space weather event, this aurora has received little attention
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This study investigates the aurora australis event of 19 March 1950, which was reported across multiple locations in Australia, including Hobart, Sydney, and as far north as Goondiwindi. Despite its significance as a historical space weather event, this aurora has received little attention in the scientific literature. Using contemporary news reports from The Sydney Morning Herald and Mercury, we reconstruct the characteristics of the event. Observers described vivid red and green auroral displays with streamers, indicative of intense geomagnetic activity. The associated geomagnetic storm reached a Kp index of 7+. We have estimated the magnetic disturbance peak of −278 nT (±15 nT) from measurements made in the Kakioka Magnetic Observatory. This would place it among the top 50 most intense storms recorded since 1957, according to the Dst index, though still significantly below the most extreme events. Notably, this aurora is absent from modern auroral catalogs, and no documented observations from the Northern Hemisphere have been identified. These findings underscore the critical role of historical records in advancing our understanding of auroral phenomena and their relationship with solar activity. Given the provisional nature of this study, further historical documents may yet emerge, providing additional insights into this event and its broader space weather context.
Full article
(This article belongs to the Special Issue Research and Space-Based Exploration on Space Plasma)
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Open AccessArticle
Influence of Building-Height Variability on Urban Ventilation and Pollutant Dispersion Characteristics
by
Taotao Shui, Lili Cao, Tieqiao Xiao and Shaojie Zhang
Atmosphere 2025, 16(5), 614; https://doi.org/10.3390/atmos16050614 (registering DOI) - 17 May 2025
Abstract
Urban densification associated with rapid urbanization has weakened horizontal ventilation in cities. Previous studies point out that building-height variability can enhance vertical ventilation, while most of them rely on idealized models that overlook the complexity of real urban environments. This study analyzes 20
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Urban densification associated with rapid urbanization has weakened horizontal ventilation in cities. Previous studies point out that building-height variability can enhance vertical ventilation, while most of them rely on idealized models that overlook the complexity of real urban environments. This study analyzes 20 actual urban blocks using CFD simulations, considering average building height, building density, and height standard deviation. The results show that areas with low-rise, uniform buildings exhibit superior pollutant dispersion, while mid- and high-rise zones experience complex turbulence and pollutant accumulation. Ventilation performance peaks when the height standard deviation ranges between 35 and 40. These findings underscore that optimizing urban form for vertical ventilation requires a combined strategy of density control and height variation. Realistic building group models more accurately capture airflow dynamics and provide valuable insights for the design of effective vertical ventilation corridors and the enhancement of urban pollutant dispersion.
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(This article belongs to the Section Air Quality)
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A Monitoring and Sampling Platform for Air Pollutants on a Rotary-Wing Unmanned Aerial Vehicle: Development and Application
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Xiaodie Kong, Xiaoya Dou, Hefan Liu, Guangming Shi, Xingyu Xiang, Qinwen Tan, Danlin Song, Fengxia Huang, Xiaoling Zhou, Hongbin Jiang, Pu Wang, Li Zhou and Fumo Yang
Atmosphere 2025, 16(5), 613; https://doi.org/10.3390/atmos16050613 (registering DOI) - 17 May 2025
Abstract
Complex air pollution, including particulate matter and ozone, is a significant environmental issue in China, with volatile organic compounds (VOCs) as key precursors. Traditional ground-based monitoring methods struggle to capture the vertical distribution and changes of pollutants in the troposphere. To address this,
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Complex air pollution, including particulate matter and ozone, is a significant environmental issue in China, with volatile organic compounds (VOCs) as key precursors. Traditional ground-based monitoring methods struggle to capture the vertical distribution and changes of pollutants in the troposphere. To address this, we developed a vertical monitoring and sampling platform using a quadcopter unmanned aerial vehicle (UAV). The platform, equipped with lightweight quartz sampling canisters and miniaturized sensors, collects air samples for VOC analysis and vertical data on meteorological parameters and particulate matter. Performance tests showed the quartz canisters had less than 15% adsorption loss, with sample storage stability exceeding 80% over three days. Sensor data showed strong correlations with standard instruments (R2 > 0.80). Computational fluid dynamics simulations optimized the sampler’s inlet position and ascertained that ascending flight mitigates rotor-induced air recirculation. Field campaigns were conducted at six sites along the Chengdu Metropolitan Circle Ring Expressway. Vertical data from 0~300 m revealed particulate matter concentrations peaked at 50~70 m. Near-surface VOCs were dominated by alkanes, while aromatics were found concentrated at 150~250 m, indicating significant regional transport influences. The results confirmed the platform’s effectiveness for pollutant distribution analysis.
Full article
(This article belongs to the Special Issue Emissions of Volatile Organic Compounds (VOCs): Characterization, Environmental Impacts and Control)
Open AccessArticle
The Development and Application of a Three-Dimensional Corona Discharge Numerical Model Considering the Thunderstorm Electric Field Polarity Reversal Process
by
Zhaoxia Wang, Bin Wu, Xiufeng Guo, Nian Zhao, He Zhang, Yubin Zhao and Yuhang Zheng
Atmosphere 2025, 16(5), 612; https://doi.org/10.3390/atmos16050612 (registering DOI) - 17 May 2025
Abstract
The study of the ground tip corona discharge is an important part of the lightning strike mechanism and lightning warning research. Because the characteristics of the corona charge distribution are difficult to observe directly, simulation research is indispensable. However, most of the previous
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The study of the ground tip corona discharge is an important part of the lightning strike mechanism and lightning warning research. Because the characteristics of the corona charge distribution are difficult to observe directly, simulation research is indispensable. However, most of the previous models have been unipolar models, which cannot reflect the characteristics of the tip corona discharge under electric field reversal during real thunderstorms. Therefore, the development of three-dimensional positive and negative corona discharge models is of great significance. In this study, a three-dimensional corona discharge numerical model considering the polarity reversal process of the electric field was developed with or without a wind field and simulated the tip corona discharge characteristics under this reversal. The reliability of the model was verified by comparing the observed results. Compared with the unipolar corona discharge model, this model could effectively evaluate the impact of the first half-cycle corona discharge on the second half-cycle opposite-polarity corona discharge and invert the spatial separation distribution characteristics of different polar corona charges released in both cycles under the influence of wind and the spatial electric field distribution characteristics generated by the corresponding corona charges. Comparing unipolar corona discharges under the same wave pattern and amplitude of the background electric field, it was assumed that the unipolar corona discharge occurred in the half cycle after the polarity reversal of an electric field, and there was also an opposite-polarity corona discharge process before it. Due to the influence of the first half cycle, the background electric field required for a corona discharge was smaller, and the corona current was generated earlier, but the end time was equivalent. At the same time, due to the neutralization effect of positive and negative corona charges, the peak value of the total corona charge in the second half cycle was significantly smaller than that of the unipolar model. At different building heights, the peak difference in the corona current and the peak difference in the corona charge between the two models increased linearly with an increase in height. It could be seen that this model had better simulation results and wider application value.
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(This article belongs to the Section Meteorology)
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Characteristics of Meteorological Droughts Across Different Climatic Zones in Benin
by
Abdoul-Aziz Bio Sidi D. Bouko, Bing Gao, Jabir Abubakar, Richard F. Annan, Randal D. Djessou, Admire M. Mutelo, Zozo El-Saadani and Lekoueiry Dehah
Atmosphere 2025, 16(5), 611; https://doi.org/10.3390/atmos16050611 (registering DOI) - 17 May 2025
Abstract
This study investigates meteorological drought characteristics across three climatic zones in Benin using the SPEI (Standardized Precipitation Evapotranspiration Index) and SPI (Standardized Precipitation Index). A comprehensive statistical approach was employed, including the Mann–Kendall trend test, drought duration and intensity analysis, Pearson correlation, cross-wavelet
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This study investigates meteorological drought characteristics across three climatic zones in Benin using the SPEI (Standardized Precipitation Evapotranspiration Index) and SPI (Standardized Precipitation Index). A comprehensive statistical approach was employed, including the Mann–Kendall trend test, drought duration and intensity analysis, Pearson correlation, cross-wavelet transform, and the Standardized Relative Air Humidity Index (SRHI), to assess drought patterns and trends. The findings indicate increasing consistency between SPI and SPEI trends at longer timescales, though significant regional variations persist. In Zone 1 (northern Benin), SPI exhibited an increasing trend across all timescales, whereas SPEI demonstrated a decreasing trend at shorter timescales. In contrast, in Zones 2 (central Benin) and 3 (south Benin), both indices generally displayed a decreasing trend, except at the one-month scale. An analysis of drought duration and intensity revealed that, at shorter timescales (SPI and SPEI at 1- and 3-month intervals), the longest droughts occurred in Zones 1 and 3, while the most intense events were recorded in Zone 2. At longer timescales (SPI and SPEI at 6- and 12-month intervals), Zone 2 experienced the longest droughts, whereas Zone 3 exhibited the highest intensities. These findings illustrate the need for monitoring strategies tailored to a given area’s characteristics. Despite these insights, data uncertainties and regional differences present challenges for drought investigation. Future studies should incorporate more datasets and investigate different drought indices to improve decision-making and improve strategies for safeguarding Benin’s agricultural sector, ecosystems, and food supply.
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(This article belongs to the Section Climatology)
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Open AccessArticle
Disparities in Fine Particulate Matter Air Pollution Exposures at the US–Mexico Border: The Intersection of Race/Ethnicity and Older Age
by
Timothy W. Collins, Colby M. Child, Sara E. Grineski and Mathilda Scott
Atmosphere 2025, 16(5), 610; https://doi.org/10.3390/atmos16050610 (registering DOI) - 17 May 2025
Abstract
Environmental justice research in the United States (US) documents greater air pollution exposures for Hispanic/Latino vs. non-Hispanic White groups. EJ research has not focused on the intersection of race/ethnicity and older age nor short-term fine particulate matter (PM2.5) exposures. We address
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Environmental justice research in the United States (US) documents greater air pollution exposures for Hispanic/Latino vs. non-Hispanic White groups. EJ research has not focused on the intersection of race/ethnicity and older age nor short-term fine particulate matter (PM2.5) exposures. We address these knowledge gaps by studying US metropolitan area census tracts within 100 km of the US–Mexico border, a region with serious air quality issues. We use US Census American Community Survey data to construct sociodemographic variables and Environmental Protection Agency Downscaler data to construct long-term and short-term measures of PM2.5 exposure. Using multivariable generalized estimating equations, we test for differences in PM2.5 exposures between census tracts with higher vs. lower proportions of older Hispanic/Latino residents and older non-Hispanic White residents. The results indicate that as the proportion of the Hispanic/Latino population ≥ 65 years of age increases, long-term and short-term PM2.5 exposures significantly increase. In contrast, as the proportion of the non-Hispanic White population ≥ 65 years of age increases, changes in long-term and short-term PM2.5 exposures are statistically non-significant. These findings illuminate how race/ethnicity and older age intersect in shaping PM2.5 exposure disparities and may inform efforts to mitigate air pollution exposures for overburdened people along the US–Mexico border.
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(This article belongs to the Section Air Quality)
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Relationships Among Atmospheric Suspended Particulates with Different Sizes: A Case Study of Chongqing City
by
Yan Gui and Haiyang Wang
Atmosphere 2025, 16(5), 609; https://doi.org/10.3390/atmos16050609 (registering DOI) - 17 May 2025
Abstract
The current study predicts that there would be scaling relationships among atmospheric suspended particulate materials (PMs) with different diameters. Through sampling the particulate materials concentration over different types of land use in municipal areas in Chongqing, analyzing the respective data of the independent
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The current study predicts that there would be scaling relationships among atmospheric suspended particulate materials (PMs) with different diameters. Through sampling the particulate materials concentration over different types of land use in municipal areas in Chongqing, analyzing the respective data of the independent concentrations of particulate materials varying in sizes, and testing the predictions, it is found that: (1) there are generally a negative relationships between falling dust of large particulate size (diameter > 10 μm) and floating dust of small ones (diameter ≤ 10 μm); (2) there are positive correlations among the fine particulate materials varying in sizes of iPM10, iPM2.5, and iPM1; (3) there is a disproportionately increase between the particulate materials varying in sizes compared to the respective control; (4) there is a declining-and-rising tendency between the particulate materials reduction rate and the increase in particulate materials along a particulate-size-decline gradient. The results of this study may contribute to understanding the law of the interactions of particulate materials with different particle sizes in the atmosphere and lay a theoretical foundation for the elimination of the atmospheric suspended pollutants.
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(This article belongs to the Special Issue Recent Advances in Urban Climate)
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Open AccessArticle
Climate Adaptability Analysis of Traditional Dwellings in Mountain Terraced Areas: A Case Study of ‘Mushroom Houses’ in the Hani Terraces of Yunnan, China
by
Luyao Hu, Yinong Liu, Xinkai Li and Pengbo Yan
Atmosphere 2025, 16(5), 608; https://doi.org/10.3390/atmos16050608 (registering DOI) - 16 May 2025
Abstract
This study examines the climate adaptability of traditional Hani ‘Mushroom Houses’ located in the rice terrace region of Honghe Hani Autonomous Prefecture, Yunnan, China. By analyzing 30 years of meteorological data, the study identifies the local climatic characteristics of high temperatures, high humidity,
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This study examines the climate adaptability of traditional Hani ‘Mushroom Houses’ located in the rice terrace region of Honghe Hani Autonomous Prefecture, Yunnan, China. By analyzing 30 years of meteorological data, the study identifies the local climatic characteristics of high temperatures, high humidity, and significant diurnal temperature variations. The thermal comfort voting method was used to establish a quantitative relationship between the Physiological Equivalent Temperature (PET) index and residents’ subjective thermal perceptions, thereby assessing seasonal variations in thermal comfort. Field measurements of indoor and outdoor temperature, humidity, and wind speed were conducted in May and December 2023 to evaluate thermal interactions between rooms. This study demonstrated: (1) the critical roles of building orientation (e.g., northwest-facing design), functional layout (e.g., multi-story zoning), and structural forms (e.g., thick walls, thatched roofs) in regulating temperature and humidity. (2) Confirmed that Hani ‘Mushroom Houses’ stabilize indoor environments through passive strategies, including material selection (wood, rammed earth), natural ventilation (cross-draft design), and spatial organization (climate-buffering storage layers). (3) Provided empirical evidence for optimizing traditional dwellings (e.g., enhanced insulation, ventilation improvements) and advancing sustainable practices in similar climatic regions.
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(This article belongs to the Section Biometeorology and Bioclimatology)
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Open AccessArticle
Analysis of Carbon Source/Sink Driving Factors Under Climate Change in the Inner Mongolia Grassland Ecosystem Through MGWR
by
Ritu Wu, Zhimin Hong, Wala Du, Hong Ying, Rihan Wu, Yu Shan, Sainbuyan Bayarsaikhan and Dan Xiang
Atmosphere 2025, 16(5), 607; https://doi.org/10.3390/atmos16050607 (registering DOI) - 16 May 2025
Abstract
Grassland ecosystems are essential components of the global ecosystem. They may efficiently reduce CO2 concentrations in the atmosphere and play a vital role in mitigating climate change. The objectives of this study were to reveal the spatial distribution features of net primary
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Grassland ecosystems are essential components of the global ecosystem. They may efficiently reduce CO2 concentrations in the atmosphere and play a vital role in mitigating climate change. The objectives of this study were to reveal the spatial distribution features of net primary production (NPP) and net ecosystem productivity (NEP) under climate change in the Inner Mongolia grassland ecosystem, China, and to devise effective management strategies for grassland ecosystems. Based on the multiscale geographically weighted regression (MGWR) model, this study investigated the spatial variation features of NPP and NEP along with their driving factors. The results showed the following: (1) The annual average NPP in the Inner Mongolia grassland ecosystem was 234.22 , and the annual average NEP was 60.31 from 2011 to 2022. Both measures showed a spatial pattern of high values in the northeast and low values in the southwest, as well as a temporal pattern of high values in summer and low values in winter. (2) The normalized difference vegetation index (NDVI) and solar radiation had promoting effects on NPP, where NDVI had the largest significant positive correlation area. In addition, precipitation and temperature on the influence of NPP were significantly negative with a larger area. (3) The area with a significant positive correlation of NDVI, solar radiation, and precipitation on NEP was larger than that with a significant negative correlation, while the area with significant negative correlation of temperature was larger. This study used the MGWR model to explore the relationship between NPP, NEP, and multiple factors. The results showed regional variation in NPP and NEP under the combined effect of various drivers. This contributes to a better understanding of carbon sinks under climate change in the Inner Mongolia grassland ecosystem.
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(This article belongs to the Special Issue Response of Vegetation to Climatic and Anthropogenic Drivers in the Plateau)
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Open AccessArticle
Analysis of Time-Domain Characteristics of Microsecond-Scale Repetitive Pulse Discharge Events in Lightning
by
Jinxing Shen, Zheng Sun, Lihua Shi and Shi Qiu
Atmosphere 2025, 16(5), 606; https://doi.org/10.3390/atmos16050606 (registering DOI) - 16 May 2025
Abstract
To clarify the background of multiple burst (MB) specifications in the aviation lightning test standards, a broadband lightning electromagnetic field measurement system was employed to collect 91 sets of VLF/LF band nature flash data. A total of 719 typical repetitive pulse (RP) groups
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To clarify the background of multiple burst (MB) specifications in the aviation lightning test standards, a broadband lightning electromagnetic field measurement system was employed to collect 91 sets of VLF/LF band nature flash data. A total of 719 typical repetitive pulse (RP) groups were identified, and 163,589 single pulse samples were analyzed statistically. The variational mode decomposition (VMD) method and a trend-free correlation on index (TFCI) were used to extract RPs from the slowly varying trends and high-frequency noises from the measured data. The time-domain characteristics of four kinds of RPs corresponding to the lightning discharge events—initial breakdown pulse (IBP), regular pulse bursts (RPB), chaotic pulse train (CPT), and dart-stepped leader (DSL)—were investigated. By comparing previous statistics and the definition in current international aviation standards, the intrinsic correlation between RPs and the defined parameters of MBs was explored. New recommendations for the MB test standard were subsequently proposed.
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(This article belongs to the Section Upper Atmosphere)
Open AccessArticle
The Risk of Developing Aphasia and Exposure to Air Pollution in Taiwan
by
Jinyi Hung, Pei-Chun Lin, Chiu-Ying Chen, Stella Chin-Shaw Tsai, Ruey-Hwang Chou, Cheng-Li Lin, Der-Yang Cho, Ching-Liang Hsieh, Chang-Yin Lee, Kuang-Hsi Chang, Yi-Chao Hsu and Tai-Lin Huang
Atmosphere 2025, 16(5), 605; https://doi.org/10.3390/atmos16050605 - 16 May 2025
Abstract
(1) Background: The relationship between air pollution and the risk of developing aphasia is still unclear. We aimed to evaluate air pollution exposure as a risk factor for developing aphasia in Taiwan. (2) Methods: This retrospective population-based cohort study used the Longitudinal Generation
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(1) Background: The relationship between air pollution and the risk of developing aphasia is still unclear. We aimed to evaluate air pollution exposure as a risk factor for developing aphasia in Taiwan. (2) Methods: This retrospective population-based cohort study used the Longitudinal Generation Tracking Database (LGTD) and the Taiwan Air Quality Monitoring Database (TAQMD). The incidence rate ratio (IRR) and adjusted hazard ratio (aHR) were calculated to examine the association between aphasia and exposure to six air pollutants: sulfur oxide (SO2), carbon monoxide (CO), nitric oxide (NO), nitrogen oxide (NOx), and particulate matter (PM2.5, PM10) from 2003 to 2017. (3) Results: The incidence rate ratio (IRR) of aphasia showed that individuals with high levels of SO2, CO, and NO were at a higher risk of developing aphasia. Increased exposure to airborne particulate matter (PM2.5 and PM10) also increased the risk of developing aphasia. The adjusted HRs of the aphasia risk were statistically significant for all the air pollutants at higher concentrations. (4) Conclusions: Individuals exposed to ambient air pollutants have a significantly higher risk of developing aphasia. The greater the exposure to airborne particulate matter and gaseous pollutants, the more likely individuals are to develop aphasia.
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(This article belongs to the Section Air Quality and Health)
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Open AccessArticle
Development and Evaluation of the Online Hybrid Model CAMx-LPiG
by
Andrea Piccoli, Valentina Agresti, Giovanni Lonati and Guido Pirovano
Atmosphere 2025, 16(5), 604; https://doi.org/10.3390/atmos16050604 - 16 May 2025
Abstract
CAMx-LPiG (Comprehensive Air Quality Model with Extensions—Linear Plume in Grid) is an online hybrid model based on the Chemistry and Transport Model (CTM) CAMx, which includes a sub-grid scale module to simulate the dispersion of linear road traffic emissions called LPiG. LPiG is
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CAMx-LPiG (Comprehensive Air Quality Model with Extensions—Linear Plume in Grid) is an online hybrid model based on the Chemistry and Transport Model (CTM) CAMx, which includes a sub-grid scale module to simulate the dispersion of linear road traffic emissions called LPiG. LPiG is a plume in grid module specifically developed by extending the capabilities of the Lagrangian puff sub-grid model available in CAMx. The online integration of the local scale model within the Eulerian CTM allows for a multiscale simulation of air quality from the regional scale to the urban scale, preserving a coherent description of the chemical state of the atmosphere at all spatial scales and avoiding any double counting of the emissions simulated by the sub-grid module. In this work, the model is presented and evaluated against measured NO2 concentrations for the city of Milan for the month of January 2017. The model can introduce road traffic-induced gradient in NO2 concentration at sub-grid resolution. Moreover, CAMx-LPiG has been shown to reduce bias compared to CAMx stand-alone simulations.
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(This article belongs to the Special Issue Urban Air Pollution, Meteorological Conditions and Human Health)
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Open AccessArticle
Seasonal and Diurnal Variations of Indoor PM2.5 in Six Households in Akure, Nigeria
by
Sawanya Saetae, Francis Olawale Abulude, Kazushi Arasaki, Mohammed Mohammed Ndamitso, Akinyinka Akinnusotu, Samuel Dare Oluwagbayide, Yutaka Matsumi, Kazuaki Kawamoto and Tomoki Nakayama
Atmosphere 2025, 16(5), 603; https://doi.org/10.3390/atmos16050603 - 16 May 2025
Abstract
Seasonal, diurnal, and site-to-site variations in indoor PM2.5 concentrations in Akure, a city in southwestern Nigeria, are investigated by continuous observations using low-cost sensors in six households. Significant seasonal variations were observed, with the highest monthly PM2.5 concentrations occurring in the
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Seasonal, diurnal, and site-to-site variations in indoor PM2.5 concentrations in Akure, a city in southwestern Nigeria, are investigated by continuous observations using low-cost sensors in six households. Significant seasonal variations were observed, with the highest monthly PM2.5 concentrations occurring in the dry season, both indoors and outdoors. Significant seasonal variations with higher PM2.5 levels during the dry season were observed, with mean PM2.5 concentrations of 55 μg/m3 in the kitchen and 48 μg/m3 in the living rooms, compared to those during the wet season (23 μg/m3 in the kitchen and 14 μg/m3 in the living rooms). The kitchen-to-outdoor and indoor-to-outdoor PM2.5 ratios increased particularly during the morning and evening hours at several sites, suggesting significant contributions from cooking activities in the kitchen, as well as the transfer of PM2.5 into the living room. An assessment of PM2.5 exposure risks among 32 residents in the studied households revealed higher risks among individuals who cook routinely. This study underscores the importance of addressing indoor air pollution alongside outdoor pollution, particularly by improving ventilation and reducing cooking emissions, to effectively minimize exposure risks.
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(This article belongs to the Section Air Quality)
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