Atmosphere

22 pages, 7360 KB

Open AccessArticle

Evaporation Duct Height Short-Term Prediction Based on Bayesian Hyperparameter Optimization

by Ye-Wen Wu, Yu Zhang, Zhi-Qiang Fan, Han-Yi Chen, Sheng-Lin Zhang and Yu-Qiang Zhang

Atmosphere 2025, 16(10), 1126; https://doi.org/10.3390/atmos16101126 (registering DOI) - 25 Sep 2025

Accurately predicting evaporation duct height (EDH) is a crucial technology for enabling over-the-horizon communication and radar detection at sea. To address the issues of overfitting in neural network training and the low efficiency of manual hyperparameter tuning in conventional evaporation duct height (EDH) [...] Read more.

Accurately predicting evaporation duct height (EDH) is a crucial technology for enabling over-the-horizon communication and radar detection at sea. To address the issues of overfitting in neural network training and the low efficiency of manual hyperparameter tuning in conventional evaporation duct height (EDH) prediction, this study proposes the application of Bayesian optimization (BO)-based deep learning techniques to EDH forecasting. Specifically, we developed a novel BO–LSTM hybrid model to enhance the predictive accuracy of EDH. First, based on the CFSv2 reanalysis data from 2011 to 2020, we employed the NPS model to calculate the hourly evaporation duct height (EDH) over the Yongshu Reef region in the South China Sea. Then, the Mann–Kendall (M–K) method and the Augmented Dickey–Fuller (ADF) test were employed to analyze the overall trend and stationarity of the EDH time series in the Yongshu Reef area. The results indicate a significant declining trend in EDH in recent years, and the time series is stationary. This suggests that the data can enhance the convergence speed and prediction stability of neural network models. Finally, the BO–LSTM model was utilized for 24 h short-term forecasting of the EDH time series. The results demonstrate that BO–LSTM can effectively predict EDH values for the next 24 h, with the prediction accuracy gradually decreasing as the forecast horizon extends. Specifically, the 1 h forecast achieves a root mean square error (RMSE) of 0.592 m, a mean absolute error (MAE) of 0.407 m, and a model goodness-of-fit (R²) of 0.961. In contrast, the 24 h forecast shows an RMSE of 2.393 m, MAE of 1.808 m, and R² of only 0.362. A comparative analysis between BO–LSTM and LSTM reveals that BO–LSTM exhibits marginally superior accuracy over LSTM for 1–15 h forecasts, with its performance advantage becoming increasingly pronounced for longer forecast horizons. This confirms that the Bayesian optimization-based hyperparameter tuning method significantly enhances model prediction accuracy. Full article

(This article belongs to the Special Issue Radar Sensing Atmosphere: Modelling, Imaging and Prediction (2nd Edition))

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19 pages, 783 KB

Open AccessArticle

Occupational Exposure Assessment of Fine Particulate Matter (PM2.5) and Respirable Crystalline Silica in the Ceramic Industry of Indonesia

by Moch Sahri, Shintia Yunita Arini, Farahul Jannah and Muhammad Amin

Atmosphere 2025, 16(10), 1125; https://doi.org/10.3390/atmos16101125 - 25 Sep 2025

Abstract

This study evaluates occupational exposure to respirable particulate matter (PM2.5) and crystalline silica (c-silica) among workers in five ceramic industries in Indonesia. Personal sampling revealed that 55.3% of workers were exposed to c-silica levels exceeding the Threshold Limit Value (TLV) of 50 µg/m [...] Read more.

This study evaluates occupational exposure to respirable particulate matter (PM2.5) and crystalline silica (c-silica) among workers in five ceramic industries in Indonesia. Personal sampling revealed that 55.3% of workers were exposed to c-silica levels exceeding the Threshold Limit Value (TLV) of 50 µg/m³, with concentrations ranging from 1.5 to 1395.3 µg/m³. PM2.5 levels reached as high as 4152.4 µg/m³ in certain production zones. Health surveys identified frequent respiratory symptoms such as shortness of breath (27.1%) and chronic cough (14.6%), with 6.4% of workers showing lung abnormalities on chest X-rays. Risk assessments based on chronic daily intake (CDI), hazard quotient (HQ), and risk quotient (RQ) revealed that 63.8% of workers faced unsafe exposure, particularly those with longer job tenures, older age, and poor compliance with personal protective equipment (PPE). To mitigate risks, the study recommends engineering controls such as more local exhaust ventilation, improved PPE usage, and administrative measures including job rotation and regular health monitoring. These findings highlight the urgent need for improved occupational health strategies in silica-intensive industries and call for further research on long-term health impacts and effective intervention programs. Full article

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

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17 pages, 5872 KB

Open AccessArticle

Characterization of Particulate Matter in Indoor Air from Cooking Activities in Rural Indonesian Households

by Muhammad Amin, Vera Surtia Bachtiar, Zarah Arwieny Hanami and Muralia Hustim

Atmosphere 2025, 16(10), 1124; https://doi.org/10.3390/atmos16101124 - 25 Sep 2025

Abstract

Indoor air pollution remains a critical health issue in the rural areas of low- and middle-income countries like Indonesia, where solid fuels are commonly used for cooking. This study assessed real-time indoor particulate matter (PM) concentrations in three rural households in Jorong V [...] Read more.

Indoor air pollution remains a critical health issue in the rural areas of low- and middle-income countries like Indonesia, where solid fuels are commonly used for cooking. This study assessed real-time indoor particulate matter (PM) concentrations in three rural households in Jorong V Botung, West Sumatra, using PurpleAir low-cost sensors (PurpleAir Inc., Draper, UT, USA). Mass concentrations of PM₁, PM_2.5, and PM₁₀, along with size-segregated number concentrations (0.3–10 µm), were monitored continuously over six days (30 March–4 April 2024) during the Eid al-Fitr holiday, which involves extensive cooking activities. PM_2.5 concentrations frequently exceeded 200 µg/m³, with a peak of 249.9 µg/m³ recorded during morning cooking hours. The smallest particle size (0.3–0.5 µm) dominated number concentrations, reaching up to 17,098 particles/dL, while larger particle levels were significantly lower. Strong positive correlations (r > 0.95) were observed among PM₁, PM_2.5, PM₁₀ and AQI, indicating that cooking emissions substantially contributed to indoor PM levels. The findings highlight the need for targeted interventions, including clean fuel subsidies, improved ventilation, and public awareness efforts. This study contributes critical data on indoor air quality in rural Indonesia and supports broader initiatives to reduce exposure to household air pollution in Southeast Asia. Full article

(This article belongs to the Special Issue Enhancing Indoor Air Quality: Monitoring, Analysis and Assessment)

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15 pages, 4435 KB

Open AccessArticle

Assessments of Satellite-Based Aerosol Optical Depth for Monitoring Air Quality of the Large Port of Busan, Korea

by Ukkyo Jeong, Serin Kim, Subin Lee, Yeonjin Jung and Sang Seo Park

Atmosphere 2025, 16(10), 1123; https://doi.org/10.3390/atmos16101123 - 25 Sep 2025

Abstract

Busan’s major port is among the largest trading ports worldwide; however, it is also one of the ten most polluted ports globally. This study aims to assess the effectiveness of satellite-derived aerosol data for monitoring particulate matter levels in Busan. Aerosol optical depth [...] Read more.

Busan’s major port is among the largest trading ports worldwide; however, it is also one of the ten most polluted ports globally. This study aims to assess the effectiveness of satellite-derived aerosol data for monitoring particulate matter levels in Busan. Aerosol optical depth (AOD) from the Visible Infrared Imaging Radiometer (VIIRS) Deep Blue product tends to be sparse near coastlines due to higher retrieval uncertainties. To increase the number of samples along the coastal area, we established optimized quality control criteria, resulting in more than three times the number of samples. The VIIRS AOD showed a positive correlation with surface particulate matter (PM_2.5) measurements (r = 0.42). The ratios of VIIRS AOD to surface PM_2.5 and PM₁₀ were higher in coastal areas, probably due to greater hygroscopic growth of particles. This connection can assist in estimating surface PM concentrations using satellite data. Both VIIRS AOD and surface PM concentrations exhibit a negative correlation with terrain elevation, primarily due to the locations of emission sources and altitude-dependent weather factors such as temperature and humidity. We expect that combining higher-resolution ancillary databases, including digital elevation maps and meteorology, with satellite-based AOD will enhance the detail of air quality evaluations in port cities. Full article

(This article belongs to the Special Issue Atmospheric Pollution in Highly Polluted Areas)

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33 pages, 13292 KB

Open AccessArticle

PM_2.5 Sink-Source Dichotomy in Urban Clusters: Land Cover Efficiency Gradients and Socio-Meteorological Interactions

by Jian Yao and Yaolong Zhao

Atmosphere 2025, 16(10), 1122; https://doi.org/10.3390/atmos16101122 - 24 Sep 2025

Abstract

Urban clusters face escalating atmospheric challenges, with elevated PM_2.5 concentrations representing a critical environmental constraint. This study investigates spatiotemporal evolution mechanisms of PM_2.5 within China’s Taiyuan-Yuci-Xinzhou (TYX) urban cluster. Daily PM_2.5 concentrations (July–September 2000–2020) characterized spatiotemporal distributions. The Urban Forest [...] Read more.

Urban clusters face escalating atmospheric challenges, with elevated PM_2.5 concentrations representing a critical environmental constraint. This study investigates spatiotemporal evolution mechanisms of PM_2.5 within China’s Taiyuan-Yuci-Xinzhou (TYX) urban cluster. Daily PM_2.5 concentrations (July–September 2000–2020) characterized spatiotemporal distributions. The Urban Forest Effects (UFORE) model integrated multisource data—remote sensing, leaf area index (LAI), wind speed, and precipitation—within a Geographically and Temporally Weighted Regression (GTWR) framework, quantifying PM_2.5 dry deposition across land cover types and identifying drivers. Key findings reveal the following: (1) PM_2.5 concentrations followed an initial-rise-then-decline trajectory, with pollution hotspots concentrated in Taiyuan City and neighboring industrial corridors; (2) Construction lands sprawl markedly elevated PM_2.5 levels, whereas green areas and agricultural lands expansion promoted deposition; water areas exhibited no significant effect; (3) Wind speeds and precipitation positively modulated PM_2.5 in green areas and agricultural lands, contrasting with NDVI’s negative influence; elevation showed null correlation, while agricultural lands deposition correlated positively with PM_2.5; (4) GDP displayed an inverted U-curve association with PM_2.5; positive correlations emerged with AVSI, TIOV, NOP, and EC indices, but negative linkage with TOC. This clarifies land cover impacts on urban atmospheric particulates, providing empirical foundations for pollution control. Full article

(This article belongs to the Section Air Quality)

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23 pages, 2959 KB

Open AccessArticle

Mercury Pollution in a Coastal City of Northern China Driven by Temperature Re-Emission, Coal Combustion, and Port Activities

by Ruihe Lyu, Liyuan Xue, Xuefang Wu, Ye Mu, Jie Cheng, Liqiu Zhou, Yuhan Wang and Roy M. Harrison

Atmosphere 2025, 16(10), 1121; https://doi.org/10.3390/atmos16101121 - 24 Sep 2025

Abstract

This study investigates the dynamics and sources of atmospheric mercury in Qinhuangdao (QHD), a coastal urban area significantly impacted by both marine and terrestrial sources. Sampling of gaseous elemental mercury (GEM), fine particle-bound mercury (PBM_2.5), and coarse particle-bound mercury (PBM_2.5 [...] Read more.

This study investigates the dynamics and sources of atmospheric mercury in Qinhuangdao (QHD), a coastal urban area significantly impacted by both marine and terrestrial sources. Sampling of gaseous elemental mercury (GEM), fine particle-bound mercury (PBM_2.5), and coarse particle-bound mercury (PBM_2.5₋₁₀) was conducted from September 2022 to August 2023. The annual mean concentrations of GEM, PBM₂.₅, and PBM_2.5₋₁₀ were 2.66, 1.01, and 0.73 ng m⁻³, respectively, with PBM levels among the highest reported for coastal cities in eastern China. GEM displayed a pronounced midday peak (12:00–14:00) with correlations to temperature (R² = 0.25–0.65) and a significant winter association with SO₂ (R² = 0.52), suggesting the combined influence of surface re-emission and coal combustion. Seasonal variations in the GEM/CO ratio (spring: 7.12; winter: 2.62) further reflected the shift between natural and combustion-related sources. PBM₂.₅ exhibited elevated concentrations (1.0–1.4 ng m⁻³) under westerly winds (~3 m s⁻¹), indicating inputs from traffic, shipping, and light industries, while PBM_2.5₋₁₀ (0.5–1.1 μg m⁻³) was strongly linked to coal-handling activities at QHD port and soil resuspension. Backward trajectory analysis showed continental air masses dominated in winter (53–100%) and maritime air masses in summer (30–50%), whereas high Hg/Na ratios in PM_2.5 (3.22 × 10⁻⁴) and PM_2.5₋₁₀ (2.17 × 10⁻⁴), far exceeding typical marine aerosol values (10⁻⁷–10⁻⁵), indicated negligible marine contributions to PBM. These findings provide new insights into the processes driving mercury pollution in coastal urban environments and highlight the critical role of port-related activities in regional mercury management. Full article

(This article belongs to the Special Issue Sources Influencing Air Pollution and Their Control)

15 pages, 4149 KB

Open AccessArticle

A Machine Learning-Based Thermospheric Density Model with Uncertainty Quantification

by Junzhi Li, Xin Ning and Yong Wang

Atmosphere 2025, 16(10), 1120; https://doi.org/10.3390/atmos16101120 - 24 Sep 2025

Abstract

Conventional thermospheric density models are limited in their ability to capture solar-geomagnetic coupling dynamics and lack probabilistic uncertainty estimates. We present MSIS-UN (NRLMSISE-00 with Uncertainty Quantification), an innovative framework integrating sparse principal component analysis (sPCA) with heteroscedastic neural networks. Our methodology leverages multi-satellite [...] Read more.

Conventional thermospheric density models are limited in their ability to capture solar-geomagnetic coupling dynamics and lack probabilistic uncertainty estimates. We present MSIS-UN (NRLMSISE-00 with Uncertainty Quantification), an innovative framework integrating sparse principal component analysis (sPCA) with heteroscedastic neural networks. Our methodology leverages multi-satellite density measurements from the CHAMP, GRACE, and SWARM missions, coupled with MSIS-00-derived exospheric temperature (tinf) data. The technical approach features three key innovations: (1) spherical harmonic decomposition of T∞ using spatiotemporally orthogonal basis functions, (2) sPCA-based extraction of dominant modes from sparse orbital sampling data, and (3) neural network prediction of temporal coefficients with built-in uncertainty quantification. This integrated framework significantly enhances the temperature calculation module in MSIS-00 while providing probabilistic density estimates. Validation against SWARM-C measurements demonstrates superior performance, reducing mean absolute error (MAE) during quiet periods from MSIS-00’s 44.1% to 23.7%, with uncertainty bounds (1σ) achieving an MAE of 8.4%. The model’s dynamic confidence intervals enable rigorous probabilistic risk assessment for LEO satellite collision avoidance systems, representing a paradigm shift from deterministic to probabilistic modeling of thermospheric density. Full article

(This article belongs to the Special Issue Radar Sensing Atmosphere: Modelling, Imaging and Prediction (2nd Edition))

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19 pages, 4701 KB

Open AccessArticle

Temporal Dynamics and Source Apportionment of PM_2.5 in a Coastal City of Southeastern China: Insights from Multiyear Analysis

by Liliang Chen, Jing Wang, Qiyuan Wang, Youwei Hong, Xinhua Wang, Wen Yang, Bin Han, Mazhan Zhuang and Zhipeng Bai

Atmosphere 2025, 16(10), 1119; https://doi.org/10.3390/atmos16101119 - 24 Sep 2025

Abstract

Xiamen, a rapidly developing coastal metropolis and tourist hub in southeastern China, faces air quality challenges due to its dense population and tourism reliance. This study investigates PM_2.5 sources and temporal variations during autumn 2013–2017 via chemical characterization, mass reconstruction, and receptor [...] Read more.

Xiamen, a rapidly developing coastal metropolis and tourist hub in southeastern China, faces air quality challenges due to its dense population and tourism reliance. This study investigates PM_2.5 sources and temporal variations during autumn 2013–2017 via chemical characterization, mass reconstruction, and receptor modeling. The Positive Matrix Factorization (PMF) model identified five sources: secondary sulfate (31%), coal/vehicle emissions (28%), industrial emissions with secondary organic aerosols (SOA, 20%), ship emissions (14%), and fugitive dust (7%). Interannual variations in source contributions highlighted impacts of anthropogenic activities, meteorology, power plant upgrades, and stricter vehicle standards. PM_2.5 declined 19% (2013–2017), driven by emission controls, while SOA surged 42% (2015–2017) due to VOC oxidation and lower temperatures. Backward trajectory and Potential Source Contribution Function (PSCF) analyses revealed significant regional transport from northern industrial zones (32% contribution) and maritime activities. Ship emissions, which have remained relatively stable over the years, underscore the need for stricter marine regulations. Fugitive dust peaked in 2015 (25.8% of PM_2.5), linked to urban construction. The findings emphasize the interplay of local emissions and regional transport in shaping PM_2.5 pollution, providing a scientific basis for targeted control strategies in coastal cities with similar socioeconomic and geographic contexts. Full article

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

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20 pages, 30882 KB

Open AccessArticle

Analysis of the Ducted Gravity Waves Generated by Elevated Convection over Complex Terrain in China

by Manman Ma and Luyao Qin

Atmosphere 2025, 16(10), 1118; https://doi.org/10.3390/atmos16101118 - 24 Sep 2025

Abstract

Gravity waves play a crucial role in the evolution of convective systems. The unique thermal structure of elevated convection occurring above a stable boundary layer facilitates the generation and propagation of gravity waves. This study focuses on an elevated convection event over Central [...] Read more.

Gravity waves play a crucial role in the evolution of convective systems. The unique thermal structure of elevated convection occurring above a stable boundary layer facilitates the generation and propagation of gravity waves. This study focuses on an elevated convection event over Central China on the night of 2–3 February 2024. The WRF model, combined with terrain sensitivity experiments, is employed to analyze the characteristics of gravity waves and the effects of terrain variability. The event consists of two elevated convection clusters; the first triggers gravity waves on its southwestern side, which subsequently initiates the second convection cluster. The gravity waves exhibit a horizontal wavelength of 25 km and a period of 17.5 min, propagating eastward. Below an altitude of 3 km, a stable wave duct layer is present, overlain by an unstable overreflective zone. This stratification creates an ideal channel for ducted gravity wave propagation, which is essential for maintaining the waves. Sensitivity experiments confirm that convective forcing alone is sufficient to generate the observed gravity waves. Although the terrain lies within the stable boundary layer, its ruggedness modulates the distribution of waves and indirectly influences the organization of elevated convection. Full article

(This article belongs to the Special Issue State-of-the-Art in Severe Weather Research)

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19 pages, 7431 KB

Open AccessArticle

Weather Regimes of Extreme Wind Speed Events in Xinjiang: A 10–30 Year Return Period Analysis

by Yajie Li, Dagui Liu, Donghan Wang, Sen Xu, Bin Ma, Yueyue Yu, Jianing Li and Yafei Li

Atmosphere 2025, 16(10), 1117; https://doi.org/10.3390/atmos16101117 - 24 Sep 2025

Abstract

Xinjiang is a critical wind energy region in China. This study characterizes extreme wind speed (EWS) events in Xinjiang by using ERA5 reanalysis (1979–2023) and station observations (2022–2023). Through k-means clustering and wind power density classification, four distinct regions and representative nodes were [...] Read more.

Xinjiang is a critical wind energy region in China. This study characterizes extreme wind speed (EWS) events in Xinjiang by using ERA5 reanalysis (1979–2023) and station observations (2022–2023). Through k-means clustering and wind power density classification, four distinct regions and representative nodes were identified, aligned with the “Three Mountains and Two Basins” topography: Region #1 (eastern wind-rich corridor), Region #2 (Tarim Basin, west–east increasing wind power density), Region #3 (northern valleys), and Region #4 (mountainous areas with weakest wind power density). Peaks-over-threshold analysis revealed 10~30-year return levels varying regionally, with 10-year return level for Node #1 reaching Beaufort Scale 11 but only Scale 6 for Node #4. Since 2001, EWS occurrences increased, with Nodes #2–4 showing doubled 10-year event occurrences in 2012–2023. Events exhibit consistent afternoon peaks and spring dominance (except Node #2 with summer maxima). Such long-term trends and diurnal and seasonal preferences of EWS could be partly explained by diverging synoptic drivers: orographic effects and enhanced pressure gradients (Node #1 and #3) associated with Ural blocking and polar vortex shifts, both showing intensification trends; thermal lows in the Tarim Basin (Node #2) accounting for their summer prevalence; boundary-layer instability that leads to localized wind intensification (Node #4). The results suggest the necessity of region-specific forecasting strategies for wind energy resilience. Full article

(This article belongs to the Special Issue Cutting-Edge Research in Severe Weather Forecast)

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15 pages, 2732 KB

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Spatiotemporal and Synoptic Analysis of PM₁₀ Based on Self-Organizing Map (SOM) During Asian Dust Events in South Korea

by Daekyeong Seong, JeongSeok Son, Dong-Ju Kim, Jongmin Yoon and Jae-Bum Lee

Atmosphere 2025, 16(10), 1116; https://doi.org/10.3390/atmos16101116 - 24 Sep 2025

Abstract

This study analyzes the spatiotemporal characteristics of PM₁₀ across 53 Asian dust events that affected the Korean Peninsula between January 2019 and June 2024. Self-Organizing Map (SOM) analysis was applied to sea level pressure and 850 hPa wind fields from the NCEP/DOE [...] Read more.

This study analyzes the spatiotemporal characteristics of PM₁₀ across 53 Asian dust events that affected the Korean Peninsula between January 2019 and June 2024. Self-Organizing Map (SOM) analysis was applied to sea level pressure and 850 hPa wind fields from the NCEP/DOE Reanalysis II dataset, classifying synoptic patterns into four distinct clusters. Cluster 1, associated with a deep low over Manchuria and strong westerly inflow, produced the highest PM₁₀ concentrations and the longest durations across most regions, with sharp afternoon peaks and the highest skewness values, and was mainly sourced from the Gobi Desert. Cluster 2 featured a high–low pressure dipole, generating localized impacts in northwestern regions and shorter durations, with moderate afternoon increases, originating primarily from the Gobi Desert and Inner Mongolia. Cluster 3, linked to a low east of Japan, resulted in elevated PM₁₀ mainly in central and southeastern regions, with peaks often occurring earlier in the day, and was associated with Manchurian dust sources. Cluster 4 exhibited a straight northwesterly flow with the high shifted eastward, producing moderate but spatially widespread concentrations and relatively consistent afternoon peaks, also linked to Manchurian sources. These results suggest that integrating synoptic pattern classification into dust forecasting can improve accuracy, enable early recognition of high-concentration events, and support the development of timely and region-specific warning strategies. Full article

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

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21 pages, 3752 KB

Open AccessArticle

Spatiotemporal Evolution of the Aridity Index and Its Latitudinal Patterns in the Lancang River Basin, China

by Liping Shan, Hangrui Zhang, Jingsheng Lei, Xiaojuan Ji, Xingji Zhu, Hang Yu and Long Wang

Atmosphere 2025, 16(10), 1115; https://doi.org/10.3390/atmos16101115 - 23 Sep 2025

Abstract

Under the context of global climate change, aridity responses exhibit significant differences across various latitudinal zones, and quantifying the dependency relationship between aridity and latitudinal zones is of great importance for differentiated water resource management. The Lancang River Basin in China spans 13 [...] Read more.

Under the context of global climate change, aridity responses exhibit significant differences across various latitudinal zones, and quantifying the dependency relationship between aridity and latitudinal zones is of great importance for differentiated water resource management. The Lancang River Basin in China spans 13 latitudinal zones with distinct altitudinal gradients, making it crucial to analyze the relationship between long-term aridity variation patterns and latitude for understanding basin hydrological response mechanisms. This study adopted the United Nations Environment Programme (UNEP) aridity index definition and utilized publicly available high-resolution datasets to divide the Chinese Lancang River Basin into 26 regions at 0.5° N intervals. The spatiotemporal evolution characteristics of the aridity index at interannual and seasonal scales from 1940 to 2022 were analyzed, and the trends of aridity index changes and their relationship with latitude were quantified. Results indicate: (1) The spring aridity index increased significantly (trend rate of 0.015/10a, Z = 2.39), driving an overall basin-wide humidification trend. (2) The aridity index exhibited significant spatial and seasonal differences with latitude: southern regions (south of 24.75° N) showed negative correlations, northern regions (north of 30.5° N) showed positive correlations, while central regions displayed distinct seasonal transitions and spatial differentiation characteristics bounded by 27.25° N. (3) The rate of aridity index change in regions north of 27.25° N was significantly higher than in southern regions (p < 0.001). This study reveals the latitudinal patterns of AI changes in the Lancang River Basin, providing guidance for developing adaptive water resource allocation strategies under climate change scenarios. Full article

(This article belongs to the Special Issue Observation and Modeling of Evapotranspiration)

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32 pages, 9657 KB

Open AccessArticle

Concentration Distribution and Physicochemical Properties of 10 nm–10 μm Coal Dust Generated by Drum Cutting Different Rank Coals: A Physical Simulation Experiment

by Hui Liu, Rong Jia, Jintuo Zhu, Liang Wang, Jiamu Tong, Yu Liu, Qingyang Tian, Wenbo Liu, Caixia An and Nkansah Benjamin Oduro

Atmosphere 2025, 16(10), 1114; https://doi.org/10.3390/atmos16101114 - 23 Sep 2025

Abstract

Shearer drum cutting of coal seams generates over half of the coal dust in coal mines, while relevant studies focus more on micron-sized dust and much less on nano- to sub-micron-sized coal dust. Based on the self-developed experimental system for simulating dust generation [...] Read more.

Shearer drum cutting of coal seams generates over half of the coal dust in coal mines, while relevant studies focus more on micron-sized dust and much less on nano- to sub-micron-sized coal dust. Based on the self-developed experimental system for simulating dust generation from drum cutting of coal bodies, this study investigated the concentration distribution characteristics and physicochemical properties of 10 nm–10 μm coal dust generated from drum cutting of different rank coals with different cutting parameters. Results showed that the coal dust mass and number concentrations were concentrated in 2–10 μm and 10–200 nm, respectively, accounting for 90% of the total 10 nm–10 μm coal dust; the mass percentages of PM1/PM10 (PM1/PM10 = PM1 particles relative to PM10 particles, similarly hereinafter), PM1/PM2.5, and PM2.5/PM10 were 3.25–4.87%, 19.35–26.73%, and 14.82–18.81%, respectively, whereas over 99% of the total number of particles in the PM10 fraction are within the PM1 fraction (i.e., N-PM1/N-PM10 > 99%), that is, both N-PM1/N-PM2.5 and N-PM2.5/N-PM10 exceeded 99%. Lower-rank coal generates less 10 nm–10 μm coal dust, and either higher moisture content, firmness coefficient, or lower fixed carbon content of the coal can effectively reduce the 10 nm–10 μm coal dust generation. Either reduction in the tooth tip cone angle, the rotary speed, or increase in the mounting angle or the cutting depth can effectively inhibit the 10 nm–10 μm coal dust generation. Higher-rank coal dust shows fewer surface pores, smoother surfaces, larger contact angles, more hydrophobic groups, and fewer hydrophilic groups. The research results have filled the knowledge gap in the pollution characteristics of nano- to submicron-sized dust generated from shearer drum cutting of coal bodies, and can serve as an important reference for the development of dust reduction and suppression technologies in coal mining faces as well as the prevention of coal worker’s pneumoconiosis. Full article

(This article belongs to the Section Air Quality)

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19 pages, 6121 KB

Open AccessArticle

Natural Variability and External Forcing Factors That Drive Surface Air Temperature Trends over East Asia

by Debashis Nath, Reshmita Nath and Wen Chen

Atmosphere 2025, 16(10), 1113; https://doi.org/10.3390/atmos16101113 - 23 Sep 2025

Abstract

Community Earth System Model-Large Ensemble (CESM-LE) simulations are used to partition the Surface Air Temperature (SAT) trends over East Asia into the contribution of external forcing factors and internal variability. In the historical period (1966–2005), the summer SAT trends display a considerable diversity [...] Read more.

Community Earth System Model-Large Ensemble (CESM-LE) simulations are used to partition the Surface Air Temperature (SAT) trends over East Asia into the contribution of external forcing factors and internal variability. In the historical period (1966–2005), the summer SAT trends display a considerable diversity (≤−2 °C to ≥2 °C) across the 35 member ensembles, while under the RCP8.5 scenario, the region is mostly dominated by a strong warming trend (~1.5–2.5 °C/51 years) and touches the ~4 °C mark by the end of the 21st century. In the historical period, the warming is prominent over the Yangtze River basin of China, while under the RCP8.5 scenario, the warming pattern shifts northward towards Mongolia. In the historical period, the Signal-to-Noise Ratio (SNR) is less than 1, while it is higher than 4 under the RCP8.5 scenario, which indicates that, in the early period, internal variability overrides the forced response and vice versa under the RCP8.5 scenario. In addition, over much of the East Asian region, the chances of cooling are relatively high in the historical period, which partially counteracted the warming trend due to external forcing factors. In contrast, under the RCP8.5 scenario, the chances of warming reach ~100% over East Asia due to contributions from the external forcing factors. The novel aspect of the current study is that, in the negative phase (from the mid-1960s to ~2000), the Atlantic Multidecadal Oscillation (AMO) accounts for ~70–80% of the cooling trend or the SAT variability over East Asia, and thereafter, natural variability exhibits a slow increasing trend in the future. However, the contribution of external forcing factors increases from ~55% in 2000 to 95% in 2075 at a rate much faster than natural variability, which is primarily due to increasing downward solar radiation fluxes and albedo feedback on SAT over East Asia. Full article

(This article belongs to the Special Issue Tropical Monsoon Circulation and Dynamics)

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