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Volume 16
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Atmosphere, Volume 16, Issue 10 (October 2025) – 84 articles

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35 pages, 2742 KB  
Article
Collaborative Station Learning for Rainfall Forecasting
by Bagati Sudarsan Patro and Prashant P. Bartakke
Atmosphere 2025, 16(10), 1197; https://doi.org/10.3390/atmos16101197 - 16 Oct 2025
Abstract
Cloudbursts and other extreme rainfall events are becoming more frequent and intense, making precise forecasts and disaster preparedness more challenging. Despite advances in meteorological monitoring, current models often lack the precision needed for hyperlocal extreme rainfall forecasts. This study addresses the research gap [...] Read more.
Cloudbursts and other extreme rainfall events are becoming more frequent and intense, making precise forecasts and disaster preparedness more challenging. Despite advances in meteorological monitoring, current models often lack the precision needed for hyperlocal extreme rainfall forecasts. This study addresses the research gap in spatial configuration-aware modeling by proposing a novel framework that combines geometry-based weather station selection with advanced deep learning architectures. The primary goal is to utilize real-time data from well-placed Automatic Weather Stations to enhance the precision and reliability of extreme rainfall predictions. Twelve unique datasets were generated using four different geometric topologies—linear, triangular, quadrilateral, and circular—centered around the target station Chinchwad in Pune, India, a site that has recorded diverse rainfall intensities, including a cloudburst event. Using common performance criteria, six deep learning models were trained and assessed across these topologies. The proposed Bi-GRU model under linear topology achieved the highest predictive accuracy (R2 = 0.9548, RMSE = 2.2120), outperforming other configurations. These findings underscore the significance of geometric topology in rainfall prediction and provide practical guidance for refining AWS network design in data-sparse regions. In contrast, the Transformer model showed poor generalization with high MAPE values. These results highlight the critical role of spatial station configuration and model architecture in improving prediction accuracy. The proposed framework enables real-time, location-specific early warning systems capable of issuing alerts 2 h before extreme rainfall events. Timely and reliable predictions support disaster risk reduction, infrastructure resilience, and community preparedness, which are essential for safeguarding lives and property in vulnerable regions. Full article
(This article belongs to the Special Issue Advanced Numerical Modeling Techniques in Meteorology: Exploring the Frontier of Weather Prediction and Data Assimilation)
29 pages, 2785 KB  
Article
Marine Boundary Layer Cloud Boundaries and Phase Estimation Using Airborne Radar and In Situ Measurements During the SOCRATES Campaign over Southern Ocean
by Anik Das, Baike Xi, Xiaojian Zheng and Xiquan Dong
Atmosphere 2025, 16(10), 1195; https://doi.org/10.3390/atmos16101195 - 16 Oct 2025
Abstract
The Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES) was an aircraft-based campaign (15 January–26 February 2018) that deployed in situ probes and remote sensors to investigate low-level clouds over the Southern Ocean (SO). A novel methodology was developed to identify cloud [...] Read more.
The Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES) was an aircraft-based campaign (15 January–26 February 2018) that deployed in situ probes and remote sensors to investigate low-level clouds over the Southern Ocean (SO). A novel methodology was developed to identify cloud boundaries and classify cloud phases in single-layer, low-level marine boundary layer (MBL) clouds below 3 km using the HIAPER Cloud Radar (HCR) and in situ measurements. The cloud base and top heights derived from HCR reflectivity, Doppler velocity, and spectrum width measurements agreed well with corresponding lidar-based and in situ estimates of cloud boundaries, with mean differences below 100 m. A liquid water content–reflectivity (LWC-Z) relationship, LWC = 0.70Z0.29, was derived to retrieve the LWC and liquid water path (LWP) from HCR profiles. The cloud phase was classified using HCR measurements, temperature, and LWP, yielding 40.6% liquid, 18.3% mixed-phase, and 5.1% ice samples, along with drizzle (29.1%), rain (3.2%), and snow (3.7%) for drizzling cloud cases. The classification algorithm demonstrates good consistency with established methods. This study provides a framework for the boundary and phase detection of MBL clouds, offering insights into SO cloud microphysics and supporting future efforts in satellite retrievals and climate model evaluation. Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
17 pages, 3785 KB  
Article
Feasibility Study of Microwave Radiometer Neural Network Modeling Method Based on Reanalysis Data
by Xuan Liu, Qinglin Zhu, Xiang Dong, Houcai Chen, Tingting Shu, Wenxin Wang and Bin Xu
Atmosphere 2025, 16(10), 1194; https://doi.org/10.3390/atmos16101194 - 16 Oct 2025
Abstract
To address the challenge of microwave radiometer modeling in regions lacking radiosonde data, this study proposes a neural network retrieval method based on high-resolution the Final Reanalysis (FNL) reanalysis data and validates its feasibility. A microwave radiometer brightness temperature–profiles retrieval model was developed [...] Read more.
To address the challenge of microwave radiometer modeling in regions lacking radiosonde data, this study proposes a neural network retrieval method based on high-resolution the Final Reanalysis (FNL) reanalysis data and validates its feasibility. A microwave radiometer brightness temperature–profiles retrieval model was developed by the Back Propagation (BP) neural network, based on FNL reanalysis data from Qingdao, China. The model’s accuracy was evaluated by comparing retrieval results with synchronous radiosonde data, with an analysis of seasonal variations. Results indicate that the Root Mean Square Error (RMSE) of temperature profiles are 1.15 °C in the near-surface layer (0–2 km) and 2.05 °C in the mid-to-upper layers (>2 km). The comprehensive RMSE for relative humidity, water vapor density, and Integrated Water Vaper (IWV) are 17.27%, 0.96 g/m3, and 1.37 mm, respectively. Overall, the errors are relatively small, and the retrieval results exhibit strong spatiotemporal consistency with radiosonde data. The error increases most rapidly within the lower atmosphere (<2 km), with distinct seasonal differences observed. Temperature and relative humidity retrieval accuracies peak in summer, whereas water vapor density and IWV retrievals perform best in winter and worst in summer. This study confirms that reanalysis data–based modeling effectively addresses the issue of limited radiosonde coverage. This method is applicable to atmospheric remote sensing in regions lacking radiosonde data, such as oceans and plateaus. It provides a feasible solution to the regional limitations of microwave radiometer applications and expands the potential uses of reanalysis data. Full article
(This article belongs to the Special Issue Radar Sensing Atmosphere: Modelling, Imaging and Prediction (2nd Edition))
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22 pages, 8353 KB  
Article
Application of Hybrid Data Assimilation Methods for Mesoscale Eddy Simulation and Prediction in the South China Sea
by Yuewen Shan, Wentao Jia, Yan Chen and Meng Shen
Atmosphere 2025, 16(10), 1193; https://doi.org/10.3390/atmos16101193 - 16 Oct 2025
Abstract
In this study, we compare two novel hybrid data assimilation (DA) methods: Localized Weighted Ensemble Kalman filter (LWEnKF) and Implicit Equal-Weights Variational Particle Smoother (IEWVPS). These methods integrate a particle filter (PF) with traditional DA methods. LWEnKF combines the PF with EnKF, while [...] Read more.
In this study, we compare two novel hybrid data assimilation (DA) methods: Localized Weighted Ensemble Kalman filter (LWEnKF) and Implicit Equal-Weights Variational Particle Smoother (IEWVPS). These methods integrate a particle filter (PF) with traditional DA methods. LWEnKF combines the PF with EnKF, while IEWVPS integrates the PF with the four-dimensional variational (4DVAR) method. These hybrid DA methods not only overcome the limitations of linear or Gaussian assumptions in traditional assimilation methods but also address the issue of filter degeneracy in high-dimensional models encountered by pure PFs. Using the Regional Ocean Model System (ROMS), the effects of different DA methods for mesoscale eddies in the northern South China Sea (SCS) are examined using simulation experiments. The hybrid DA methods outperform the linear deterministic variational and Kalman filter methods: compared to the control experiment (no assimilation), EnKF, LWEnKF, IS4DVar and IEWVPS reduce the sea level anomaly (SLA) root-mean-squared error (RMSE) by 55%, 65%, 65% and 80%, respectively, and reduce the sea surface temperature (SST) RMSE by 77%, 78%, 74% and 82%, respectively. In the short-term assimilation experiment, IEWVPS exhibits superior performance and greater stability compared to 4DVAR, and LWEnKF outperforms EnKF (LWEnKF’s posterior SLA RMSE is 0.03 m, lower than EnKF’s value of 0.04 m). Long-term forecasting experiments (16 days, starting on 20 July 2017) are also conducted for mesoscale eddy prediction. The variational methods (especially IEWVPS) perform better in simulating the flow field characteristics of eddies (maintaining accurate eddy structure for the first 10 days, with an average SLA RMSE of 0.05 m in the studied AE1 eddy region), while the filters are more advantageous in determining the total root-mean-squared error (RMSE), as well as the temperature under the sea surface. Overall, compared to EnKF and 4DVAR, the hybrid DA methods better predict mesoscale eddies across both short- and long-term timescales. Although the computational costs of hybrid DA are higher, they are still acceptable: specifically, IEWVPS takes approximately 907 s for a single assimilation cycle, whereas LWEnKF only takes 24 s, and its assimilation accuracy in the later stage can approach that of IEWVPS. Given the computational demands arising from increased model resolution, these hybrid DA methods have great potential for future applications. Full article
(This article belongs to the Special Issue Advanced Numerical Modeling Techniques in Meteorology: Exploring the Frontier of Weather Prediction and Data Assimilation)
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21 pages, 3734 KB  
Article
Characterization of VOC Emissions Based on Oil Depots Source Profiles Observations and Influence of Ozone Numerical Simulation
by Weiming An, Jilong Tong, Lei Zhang, Lingyun Ma, Yongle Liu, Hong Yang and Min Chen
Atmosphere 2025, 16(10), 1192; https://doi.org/10.3390/atmos16101192 - 16 Oct 2025
Abstract
Oil depots are continuous sources of volatile organic compounds (VOCs), which contribute to ground-level ozone (O3) and secondary organic aerosol formation, posing threats to air quality and public health. This study investigated typical crude and refined oil depots in the Xigu [...] Read more.
Oil depots are continuous sources of volatile organic compounds (VOCs), which contribute to ground-level ozone (O3) and secondary organic aerosol formation, posing threats to air quality and public health. This study investigated typical crude and refined oil depots in the Xigu District of Lanzhou by measuring VOC source profiles and establishing an emission inventory. The maximum incremental reactivity (MIR) method was applied to assess the chemical reactivity of VOCs; both the emission inventory and VOC profiles were incorporated into the WRF-CMAQ model for numerical simulations. Results showed that the average ambient VOC concentrations were 49.8 μg/m3 for the crude oil depot and 66.1 μg/m3 for the refined oil depot. The crude oil depot was dominated by alkanes (37.1%), aromatics (25.1%), and OVOCs (22.5%), while the refined oil depot was dominated by alkanes (57.3%) and OVOCs (16.7%), with isopentane identified as the most abundant species in both depots. The ozone formation potentials (OFPs) of the crude oil and refined oil depots were 153.1 μg/m3 and 178.3 μg/m3, respectively. Aromatics (47.0%) and OVOCs (29.0%) were the primary contributors at the crude oil depot, with isopentane, o-xylene, etc., as the dominant reactive species. In the refined oil depot, the main contributors were alkanes (27.8%), alkenes and alkynes (26.6%), OVOCs (24.5%), and aromatics (20.5%), among which isopentane, trans-2-butene, etc., were most prominent. In 2023, VOC emissions from the crude oil and refined oil depots were estimated at 1605.3 t and 1287.8 t, respectively, mainly from working loss (96.6%) in the crude oil depot and deck fitting loss (60.7%) and working loss (31.3%) in the refined oil depot. Numerical simulations indicated that oil depot emissions could increase regional MDA8 O3 concentrations by up to 40.0 μg/m3. At the nearby Lanlian Hotel site, emissions contributed 15.1% of the MDA8 O3, equivalent to a 6.1 μg/m3 increase, while the citywide average was 1.7 μg/m3. This study enriches the VOC source profile database for oil depots, reveals their significant role in regional O3 formation, and provides a scientific basis for precise O3 control and differentiated emission reduction strategies in Northwest China. Full article
(This article belongs to the Special Issue Air Pollution: Emission Characteristics and Formation Mechanisms)
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12 pages, 36890 KB  
Article
Big L Days in GNSS TEC Data
by Klemens Hocke and Guanyi Ma
Atmosphere 2025, 16(10), 1191; https://doi.org/10.3390/atmos16101191 - 16 Oct 2025
Abstract
Big L days are days when the lunar semidiurnal variation M2 in the ionosphere is strongly enhanced by a factor of 2 or more. The worldwide network of ground-based receivers for the Global Navigation Satellite System (GNSS) has monitored the ionospheric total [...] Read more.
Big L days are days when the lunar semidiurnal variation M2 in the ionosphere is strongly enhanced by a factor of 2 or more. The worldwide network of ground-based receivers for the Global Navigation Satellite System (GNSS) has monitored the ionospheric total electron content (TEC) since 1998. The derived world maps of TEC are provided by the International GNSS Service (IGS) and allow the study of the characteristics of big L days in TEC. In the data analysis, the signal of the lunar semidiurnal variation M2 in TEC is separated from the solar semidiurnal variation S2 by means of windowing in the spectral domain. The time series of the M2 amplitude often shows enhancements of M2 (big L days) a few days after sudden stratospheric warmings (SSWs). The M2 amplitude can reach values of 8 TECU. The M2 composite of all SSWs from 1998 to 2024 shows that the M2 amplitude is enhanced after the central date of the SSW. Regions in Southern China and South America show stronger effects of big L days. Generally, the effects of big L days on TEC show latitudinal and longitudinal dependencies. Full article
(This article belongs to the Special Issue Ionospheric Disturbances and Space Weather)
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30 pages, 1806 KB  
Article
Assessing Management Tools to Mitigate Carbon Losses Using Field-Scale Net Ecosystem Carbon Balance in a Ley-Arable Crop Sequence
by Marie-Sophie R. Eismann, Hendrik P. J. Smit, Friedhelm Taube and Arne Poyda
Atmosphere 2025, 16(10), 1190; https://doi.org/10.3390/atmos16101190 - 15 Oct 2025
Abstract
Agricultural land management is a major determinant of terrestrial carbon (C) fluxes and has substantial implications for greenhouse gas (GHG) mitigation strategies. This study evaluated the net ecosystem carbon balance (NECB) of an agricultural field in an organic integrated crop–livestock system (ICLS) with [...] Read more.
Agricultural land management is a major determinant of terrestrial carbon (C) fluxes and has substantial implications for greenhouse gas (GHG) mitigation strategies. This study evaluated the net ecosystem carbon balance (NECB) of an agricultural field in an organic integrated crop–livestock system (ICLS) with a ley-arable rotation in northern Germany over two years (2021–2023). Carbon dioxide (CO2) fluxes were measured using the eddy covariance (EC) method to derive net ecosystem exchange (NEE), gross primary production (GPP), and ecosystem respiration (RECO). This approach facilitated an assessment of the temporal dynamics of CO2 exchange, alongside detailed monitoring of field-based C imports, exports, and management activities, of a crop sequence including grass-clover (GC) ley, spring wheat (SW), and a cover crop (CC). The GC ley acted as a consistent C sink (NECB: −1386 kg C ha−1), driven by prolonged photosynthetic activity and moderate biomass removal. In contrast, the SW, despite high GPP, became a net source of C (NECB: 120 kg C ha−1) due to substantial export via harvest. The CC contributed to C uptake during the winter period. However, cumulatively, it acted as a net CO2 source, likely due to drought conditions following soil cultivation and CC sowing. Soil cultivation events contributed to short-term CO2 pulses, with their magnitude modulated by soil water content (SWC) and soil temperature (TS). Overall, the site functioned as a net C sink, with an average NECB of −702 kg C ha−1 yr−1. This underscores the climate mitigation potential of management practices such as GC ley systems under moderate grazing, spring soil cultivation, and the application of organic fertilizers. To optimize CC benefits, their use should be combined with reduced soil disturbance during sowing or establishment as an understory. Additionally, C exports via harvests could be offset by retaining greater amounts of harvest residues onsite. Full article
(This article belongs to the Section Biosphere/Hydrosphere/Land–Atmosphere Interactions)
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17 pages, 4241 KB  
Article
Spatiotemporal Dynamics of Forest Fire Risk in Southeastern China Under Climate Change: Hydrothermal Drivers and Future Projections
by Dapeng Gong and Min Jing
Atmosphere 2025, 16(10), 1189; https://doi.org/10.3390/atmos16101189 - 15 Oct 2025
Abstract
Forest fire regimes are undergoing systematic reorganization under climate change, particularly in monsoon–human coupled ecosystems such as Southeastern China, where risk dynamics remain poorly quantified. This study proposes a meteorology-driven machine learning model designed to assess long-term forest fire risk. Using kernel density [...] Read more.
Forest fire regimes are undergoing systematic reorganization under climate change, particularly in monsoon–human coupled ecosystems such as Southeastern China, where risk dynamics remain poorly quantified. This study proposes a meteorology-driven machine learning model designed to assess long-term forest fire risk. Using kernel density estimation and standard deviational ellipse analysis, we assessed the spatiotemporal patterns of fire risk during the observational period and their future shifts across the SSP1-2.6 and SSP5-8.5 scenarios. The results indicate a significant overall decline in fire frequency from 2008 to 2024 (−467.3 fires/year, representing an annual average reduction of 10.8%, p < 0.001), which is attributed primarily to enhanced regional fire prevention and control measures, yet with a notable reversal after 2016 in Guangdong and Fujian. Fires are highly seasonal, with 74% occurring in the dry season (December–March). The meteorologically driven random forest model exhibited excellent performance (R2 = 0.889), validating meteorological conditions as key drivers of regional fire dynamics. It is projected that intensified warming (+5.5 °C under SSP5-8.5) and increased precipitation variability (+23%) are likely to drive pronounced northward and inland migration in high-risk zones. Our projections indicate that by the end of the century, high-risk area coverage could expand to 19.2%, with a shift from diffuse to clustered patterns, particularly in Jiangsu and Zhejiang. These findings underscore the critical role of hydrothermal reconfiguration in reshaping fire risk geography and highlight the need for dynamic, region-specific fire management strategies in response to compound climate risks. Full article
(This article belongs to the Section Climatology)
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21 pages, 3299 KB  
Article
CHIRTS Gridded Air Temperature Downscaling Integrating MODIS Land Surface Temperature Estimates in Machine-Learning Models
by Elvis Uscamayta-Ferrano, Frédéric Satgé, Ramiro Pillco-Zolá, Henrique Roig, Diego Tola-Aguilar, Mayra Perez-Flores, Lautaro Bustillos, Fara. P. M. Rakotomandrindra, Zo Rabefitia and Simon. D. Carrière
Atmosphere 2025, 16(10), 1188; https://doi.org/10.3390/atmos16101188 - 15 Oct 2025
Abstract
Due to its sensitivity to topographic and land use land cover features, air temperature (maximum, minimum, and mean—Tx, Tn, and Tmean) is extremely variable in space and time. The sparse and unevenly distributed meteorological stations observed across [...] Read more.
Due to its sensitivity to topographic and land use land cover features, air temperature (maximum, minimum, and mean—Tx, Tn, and Tmean) is extremely variable in space and time. The sparse and unevenly distributed meteorological stations observed across remote regions cannot monitor such variability. Freely available, gridded temperature datasets (T-datasets) are positioned as an opportunity to overcome this issue. Still, their coarse spatial resolution (i.e., ≥5 km) does not allow for the observation of air temperature variations on a fine spatial scale. In this context, a set of variables that have a close relationship with daily air temperature (MODIS maximum, minimum, and mean Land Surface Temperature—LSTx, LSTn, and LSTmean; MODIS NDVI; SRTM topographic features—elevation, slope, and aspect) are integrated in three regression machine-learning models (Random Forest—RF, eXtreme Gradient Boosting—XGB, Multiple Linear Regression—MLR) to propose a T-dataset estimates (Tx, Tn, and Tmean) spatial resolution downscaling framework. The approach consists of two main steps: firstly, the machine-learning models are trained at the native 5 km spatial resolution of the studied T-dataset (i.e., CHIRTS); secondly, the application of the trained machine-learning models at a 1 km spatial resolution to downscale CHIRTS from 5 km to 1 km. The results show that the method not only improves the spatial resolution of the CHIRTS dataset, but also its accuracy, with higher improvements for Tn than for Tx and Tmean. Among the considered models, RF performs the best, with an R2, RMSE, and MAE improvement of 2.6% (0%), 47.1% (6.1%), and 55.3% (7%) for Tn (Tx). These results will support air temperature monitoring and related extreme events such as heat and cold waves, which are of prime importance in the actual climate change context. Full article
(This article belongs to the Section Meteorology)
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22 pages, 2635 KB  
Article
Analysis of Forest Fire Emissions and Meteorological Impacts in Southwestern China Based on Multi-Source Satellite Observations
by Lingli Fang, Yu Han, Junbo Lin and Wenkai Guo
Atmosphere 2025, 16(10), 1187; https://doi.org/10.3390/atmos16101187 - 15 Oct 2025
Abstract
Amid the growing frequency of forest fires in southwestern China, this study aims to quantify pollutant emissions and identify key meteorological drivers using multi-source satellite data. Active fire data from Himawari-8/9, MODIS, and VIIRS were integrated to construct a top-down emission inventory for [...] Read more.
Amid the growing frequency of forest fires in southwestern China, this study aims to quantify pollutant emissions and identify key meteorological drivers using multi-source satellite data. Active fire data from Himawari-8/9, MODIS, and VIIRS were integrated to construct a top-down emission inventory for 2016–2023, while the Geodetector method was applied to evaluate meteorological influences. Results indicate mean annual emissions (×103 t·a−1) of 5623.58 (±1554.33) for CO2, 356.84 (±98.63) for CO, and substantial amounts of particulate and gaseous pollutants. Spatially, Yunnan and Sichuan were the dominant emitters; temporally, emissions peaked in January–April and November–December, with daytime levels surpassing nighttime levels. Relative humidity was identified as the dominant meteorological driver (Q = 0.1223), while the interaction between temperature and relative humidity (Q = 0.1486) further enhanced explanatory power. These findings improve the precision of emission inventories and provide essential support for regional fire management and air quality modeling in complex environments. Full article
(This article belongs to the Topic Atmospheric Chemistry, Aging, and Dynamics)
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15 pages, 4462 KB  
Article
Study on the Interaction Effect of Negative Air Ions and Nitrogen Oxide Concentrations in Urban Forest Ecosystems Driven by Meteorological Factors
by Shaoning Li, Xiaotian Xu, Di Yu, Weikang Zhang, Siqi Wu, Na Zhao, Bin Li and Shaowei Lu
Atmosphere 2025, 16(10), 1186; https://doi.org/10.3390/atmos16101186 - 15 Oct 2025
Abstract
The correlation between negative air ions (NAI) and nitrogen oxides (NOx) exhibits significant seasonal characteristics and is non-static. Previous studies have shown that NAI concentration is highly sensitive to meteorological factors, while NOx concentration is also affected by meteorological factors, resulting in potential [...] Read more.
The correlation between negative air ions (NAI) and nitrogen oxides (NOx) exhibits significant seasonal characteristics and is non-static. Previous studies have shown that NAI concentration is highly sensitive to meteorological factors, while NOx concentration is also affected by meteorological factors, resulting in potential differences in their correlation under different meteorological conditions. To deepen the understanding of this relationship, this study explored the impact of different meteorological factors on the correlation between NAI and NOx. The main conclusions are as follows: (1) The interaction between NAI and NOx in urban forests is regulated by meteorological factors; the higher the temperature, humidity, and solar radiation, the larger the correlation coefficient, and the stronger the negative correlation between the two; (2) Under synergistic meteorological conditions, NAI concentration is high and NOx concentration is moderate, which is suitable for outdoor activities: Condition 1 is temperature > 20 °C, humidity 30–60%, air pressure > 940 kPa, solar radiation 30–60 W·m−2, wind speed < 1 m·s−1; Condition 2 is temperature > 20 °C, humidity > 60%, air pressure > 940 kPa, solar radiation > 60 W·m−2, wind speed < 1 m·s−1 (based on NAI and NOx concentration data and health standards: NAI ≥ 1000 cm−3 is beneficial to health, and NOx ≤ 80 μg/m3 meets WHO limits); (3) Temperature, humidity, and air pressure have regulatory effects on the relationship between NAI and NOx, among which air pressure exerts positive regulation, while temperature and humidity exert negative regulation. Full article
(This article belongs to the Section Air Quality)
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22 pages, 910 KB  
Article
Perception of Dry Air: Links to the Indoor Environment and Respiratory and Allergic Symptoms Among Occupants
by Xin Li, Yuexia Sun, Huiyan Deng and Juan Wang
Atmosphere 2025, 16(10), 1185; https://doi.org/10.3390/atmos16101185 - 14 Oct 2025
Abstract
Perceived dry air is a common complaint in indoor environments, yet its health associations and environmental factors related to this perception are unclear. We surveyed 7865 families and measured the indoor environment in 399 dwellings in Tianjin, China, from 2013 to 2016. It [...] Read more.
Perceived dry air is a common complaint in indoor environments, yet its health associations and environmental factors related to this perception are unclear. We surveyed 7865 families and measured the indoor environment in 399 dwellings in Tianjin, China, from 2013 to 2016. It was found that 10% of the surveyed families reported frequently perceived dry air. The dry air perception was significantly associated with wheeze (adjusted odds ratio (AOR) = 2.60), rhinitis (AOR = 1.91), eczema (AOR = 1.89), and common cold infections (AOR = 1.64) in children and sick building syndrome symptoms in adults (AOR: 2.63–8.59). Higher concentrations of di-isobutyl (DiBP) and benzyl butyl phthalate (BBzP) were observed in homes with dry air perception. Although higher relative humidity might reduce the perception of dry air (AOR = 0.66), lower air exchange rates attenuated the protective effect. Additionally, building characteristics related to pollution exposures, such as living near highways (AOR = 1.31), visible mold spots (AOR = 1.50), and suspected moisture problems (AOR = 1.88), were associated with indoor dry air perception. Our findings suggest that perceived dry air was correlated with indoor exposure to pollution and could be used as an indicator for sick buildings. Full article
(This article belongs to the Special Issue Interactions Between Indoor Environmental Quality, Occupant Behaviour, and Building Performance Under a Changing Climate)
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26 pages, 11786 KB  
Article
Quantification of Multi-Source Road Emissions in an Urban Environment Using Inverse Methods
by Panagiotis Gkirmpas, George Tsegas, Giannis Ioannidis, Paul Tremper, Till Riedel, Eleftherios Chourdakis, Christos Vlachokostas and Nicolas Moussiopoulos
Atmosphere 2025, 16(10), 1184; https://doi.org/10.3390/atmos16101184 - 14 Oct 2025
Abstract
The spatial quantification of multiple sources within the urban environment is crucial for understanding urban air quality and implementing measures to mitigate air pollution levels. At the same time, emissions from road traffic contribute significantly to these concentrations. However, uncertainties arise when assessing [...] Read more.
The spatial quantification of multiple sources within the urban environment is crucial for understanding urban air quality and implementing measures to mitigate air pollution levels. At the same time, emissions from road traffic contribute significantly to these concentrations. However, uncertainties arise when assessing the contribution of multiple sources affecting a single receptor. This study aims to evaluate an inverse dispersion modelling methodology that combines Computational Fluid Dynamics (CFD) simulations with the Metropolis–Hastings Markov Chain Monte Carlo (MCMC) algorithm to quantify multiple traffic emissions at the street scale. This approach relies solely on observational data and prior information on each source’s emission rate range and is tested within the Augsburg city centre. To address the absence of extensive measurement data of a real pollutant correlated with traffic emissions, a synthetic observational dataset of a theoretical pollutant, treated as a passive scalar, was generated from the forward dispersion model, with added Gaussian noise. Furthermore, a sensitivity analysis also explores the influence of sensor configuration and prior information on the accuracy of the emission estimates. The results indicate that, when the potential emission rate range is narrow, high-quality predictions can be achieved (ratio between true and estimated release rates, Δq≤2) even with networks using data from only 10 sensors. In contrast, expanding the allowable emission range leads to reduced accuracy (2≤Δq≤6), particularly in networks with fewer than 50 sensors. Further research is recommended to assess the methodology’s performance using real-world measurements. Full article
(This article belongs to the Section Air Quality)
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33 pages, 6714 KB  
Article
Spatiotemporal Characterization of Atmospheric Emissions from Heavy-Duty Diesel Trucks on Port-Connected Expressways in Shanghai
by Qifeng Yu, Lingguang Wang, Siyu Pan, Mengran Chen, Kun Qiu and Xiqun Huang
Atmosphere 2025, 16(10), 1183; https://doi.org/10.3390/atmos16101183 - 14 Oct 2025
Abstract
Heavy-duty diesel trucks (HDDTs) are recognized as significant sources of air pollutants and greenhouse gases (GHGs) along freight corridors in port cities. Despite their impact, few studies have provided detailed spatiotemporal insights into their emissions within port-adjacent highway systems. This study presents a [...] Read more.
Heavy-duty diesel trucks (HDDTs) are recognized as significant sources of air pollutants and greenhouse gases (GHGs) along freight corridors in port cities. Despite their impact, few studies have provided detailed spatiotemporal insights into their emissions within port-adjacent highway systems. This study presents a high-resolution, hourly emission inventory at the road-segment level for six major expressways in Shanghai, one of China’s leading port cities. The emission estimates are derived using a locally adapted COPERT V model, calibrated with HDDT GPS trajectory data and detailed road network information from OpenStreetMap. The inventory quantifies emissions of CO2, NOx, CO, PM, and VOCs, highlighting distinct temporal and spatial variation patterns. Weekday emissions consistently exceed those of weekends, with three prominent traffic-related peaks occurring between 5:00–7:00, 10:00–12:00, and 14:00–16:00. Spatial analysis identifies the G1503 and S20 expressways as major emission corridors, with S20 exhibiting particularly high emission intensity relative to its length. Combined spatiotemporal patterns reveal that weekday emission hotspots are more concentrated, reflecting typical freight activity cycles such as morning dispatch and afternoon return. The findings provide a scientific basis for formulating more precise emission control measures targeting HDDT operations in urban port environments. Full article
(This article belongs to the Special Issue Traffic Related Emission (3rd Edition))
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18 pages, 17672 KB  
Article
Event-Based Tracking of Spatiotemporally Contiguous PM2.5 Pollution Events in China
by Zhihua Zhu, Rongjian Li, Yiming Chen, Zhenlin Zhang, Yiying Guo, Bo Xiong and Yanhui Zheng
Atmosphere 2025, 16(10), 1182; https://doi.org/10.3390/atmos16101182 - 14 Oct 2025
Abstract
PM2.5 pollution events evolve continuously through spatiotemporal diffusion. However, their three-dimensional spatiotemporal variation characteristics are often overlooked, and the interactions among key characteristics (e.g., duration, maximum concentration) have not yet been systematically analyzed. This study established a three-dimensional (longitude, latitude, and time) [...] Read more.
PM2.5 pollution events evolve continuously through spatiotemporal diffusion. However, their three-dimensional spatiotemporal variation characteristics are often overlooked, and the interactions among key characteristics (e.g., duration, maximum concentration) have not yet been systematically analyzed. This study established a three-dimensional (longitude, latitude, and time) spatiotemporal framework for identifying contiguous PM2.5 pollution events based on the high-resolution ChinaHighAirPollutants (CHAP) dataset (1 km spatial and 1-day temporal resolution). The framework applied the meteorological event tracking algorithm (i.e., the Forward-in-Time method) to track PM2.5 pollution events. Based on this framework, we systematically tracked and characterized the spatiotemporal evolution of PM2.5 events across China from 2013 to 2021, quantified the relationships among key event characteristics, and tracked their transport pathways. The results show that: (1) The combination of the FiT algorithm and CHAP dataset enables effective tracking and identification of the three-dimensional spatiotemporal evolution of PM2.5 pollution events across China. (2) Event PM2.5 totals, average totals per event and pollution events exhibit a distinct right-inclined “T”-shaped pattern, with hotspots located in Xinjiang, the Beijing-Tianjin-Hebei (BTH) region, Shandong, and Henan, where annual event frequency exceeds 15. (3) Event PM2.5 totals show strong correlations with average duration per event and average maximum concentration per event, particularly in heavily polluted areas where the Pearson correlation coefficient is close to 1. (4) PM2.5 pollution events are mainly characterized by short durations of 1 day or 2–3 days, accounting for over 80% of occurrences. Long-duration events are mostly concentrated in areas with severe pollution problems, and their persistence is closely linked to spatial coverage, terrain barrier effects, and meteorological conditions. (5) PM2.5 pollution events consistently exhibit a west-to-east transport pattern. Short-duration events propagate slower across the inland northwest, whereas long-duration events show a pronounced increase in meridional transport speeds along the eastern coastal areas. This study elucidates the continuous spatiotemporal evolution and intrinsic drivers of PM2.5 pollution events, offering scientific insights to support air quality improvement and the development of targeted management strategies. Full article
(This article belongs to the Special Issue Air Pollution in China (4th Edition))
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17 pages, 4562 KB  
Article
Retrieval of Atmospheric Visibility and Its Driving Factors in Shanghai, China
by Xiaowen Gui, Jing Ren, Guoyin Wang, Yuying Wang, Miao Zhang and Xiaoyan Wang
Atmosphere 2025, 16(10), 1181; https://doi.org/10.3390/atmos16101181 - 14 Oct 2025
Viewed by 3
Abstract
The combined effects of meteorological factors and aerosol chemical compositions on atmospheric visibility in Shanghai were investigated in this study based on the observed hourly dataset during 2022–2024. Correlation analysis and random forest modeling are employed to quantify the relative contributions of these [...] Read more.
The combined effects of meteorological factors and aerosol chemical compositions on atmospheric visibility in Shanghai were investigated in this study based on the observed hourly dataset during 2022–2024. Correlation analysis and random forest modeling are employed to quantify the relative contributions of these factors. The results reveal significant negative correlations between visibility and both PM2.5 concentration and relative humidity, with partial correlation coefficient of −0.62 and −0.61. Nitrate, ammonium, and other aerosol components substantially modulate these relationships. The random forest model explains 83% of the variance when only meteorological variables are considered, increasing to 93% with the inclusion of aerosol chemical composition. Under 30 km high-visibility conditions, PM2.5 is the dominant predictor (39%) of atmospheric visibility variation, followed by relative humidity (35%). In contrast, during low-visibility conditions (lower than 7.5 km), relative humidity becomes the primary contributor (30%), the influence of PM2.5 weakens (18%), and aerosol chemical components account for a larger share (30%). These findings provide important insights into the mechanisms governing visibility variability under different environmental conditions. Full article
(This article belongs to the Section Air Quality)
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23 pages, 13998 KB  
Article
Vegetation Transpiration Drives Root-Zone Soil Moisture Depletion in Subtropical Humid Regions: Evidence from GLDAS Catchment Simulations in Fujian Province
by Yudie Xie, Yali Wang, Dina Huang, Xingwei Chen and Haijun Deng
Atmosphere 2025, 16(10), 1180; https://doi.org/10.3390/atmos16101180 - 13 Oct 2025
Viewed by 195
Abstract
Understanding the relationship between vegetation transpiration and root-zone soil moisture is essential for assessing eco-hydrological processes under global change. However, past studies often looked at only one side, and traditional field observations have the limitations of high cost and poor spatial–temporal continuity. Using [...] Read more.
Understanding the relationship between vegetation transpiration and root-zone soil moisture is essential for assessing eco-hydrological processes under global change. However, past studies often looked at only one side, and traditional field observations have the limitations of high cost and poor spatial–temporal continuity. Using daily GLDAS Catchment data from 2004 to 2023, this study investigates the spatiotemporal patterns and interactions between vegetation transpiration and root-zone soil moisture in Fujian Province. The results show that transpiration decreased before 2016 and increased thereafter temporally, with an overall spatial decline. In contrast, the root-zone soil moisture increased before 2016 and then decreased temporally, showing overall spatial growth with significant heterogeneity. A strong negative correlation was found between vegetation transpiration and root-zone soil moisture, particularly in summer and autumn. Among them, vegetation transpiration strongly influenced soil moisture, with increases (or decreases) in transpiration corresponding to decreases (or increases) in soil moisture. Moreover, transpiration changes preceded those in soil moisture, and a significant resonance relationship with a 1- to 2-year cycle was identified. These findings offer insights into the vegetation–soil moisture dynamics in humid subtropical regions, supporting eco-hydrological management under climate change. Full article
(This article belongs to the Section Biometeorology and Bioclimatology)
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22 pages, 5888 KB  
Article
Weather-Regime-Based Heatwave Risk Typing and Urban Climate Resilience Assessment in New Delhi (1997–2016)
by Yukai Li, Chenglong Zhong, Zhen Deng and Zeyun Jiang
Atmosphere 2025, 16(10), 1179; https://doi.org/10.3390/atmos16101179 - 13 Oct 2025
Viewed by 165
Abstract
Extreme heat across the North Indian Plain has intensified in recent decades, with the temperature in Delhi repeatedly exceeding 48 °C. We present a physically interpretable and computationally efficient typology of heatwave risk using aggregated station observations of daily mean temperature, relative humidity, [...] Read more.
Extreme heat across the North Indian Plain has intensified in recent decades, with the temperature in Delhi repeatedly exceeding 48 °C. We present a physically interpretable and computationally efficient typology of heatwave risk using aggregated station observations of daily mean temperature, relative humidity, wind speed, and pressure from 1997 to 2016. Quality-controlled, standardized daily features (PCA-verified) were clustered with k-means; internal validity indices (Silhouette, Calinski–Harabasz, and Davies–Bouldin) identified an optimal partition with k = 3, defining three distinct weather regimes. Coupling these regimes with an absolute heatwave criterion (daily mean ≥30 °C for ≥3 days) revealed a pronounced gradient: a dry–hot, high-pressure regime (41% of days) accounted for 63% of heatwave days (mean 33.4 °C; median duration ≈17 days); a mild–humid background (59%) yielded ~8% incidence; and a rare blocking-driven dry intrusion (<1%) produced heatwaves each time, with mean temperatures of >35 °C and episodes persisting for ≥30 days. Regime–heatwave relationships were statistically significant and robust across sensitivity tests, including variations in k, alternative clustering algorithms, and bootstrap resampling. This four-stage workflow consists of data preparation, feature extraction, regime classification, and heatwave risk attribution and provides a transparent basis for regime-aware early warning, demand-side energy management, and public health protection in Delhi and is transferable to other rapidly urbanizing regions. Full article
(This article belongs to the Section Climatology)
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32 pages, 1052 KB  
Article
Transit-Oriented Development Urban Spatial Forms and Typhoon Resilience in Taipei: A Dynamic Analytic Network Process Evaluation
by Chia-Nung Li, Yi-Kai Hsieh and Chien-Wen Lo
Atmosphere 2025, 16(10), 1178; https://doi.org/10.3390/atmos16101178 - 13 Oct 2025
Viewed by 219
Abstract
Taipei’s metropolitan region faces frequent typhoon impacts that test its urban resilience. This study examines the relationship between Transit-Oriented Development (TOD) urban spatial forms and Taipei’s resilience against typhoons, considering both physical urban morphology and planning factors. We apply a Dynamic Analytic Network [...] Read more.
Taipei’s metropolitan region faces frequent typhoon impacts that test its urban resilience. This study examines the relationship between Transit-Oriented Development (TOD) urban spatial forms and Taipei’s resilience against typhoons, considering both physical urban morphology and planning factors. We apply a Dynamic Analytic Network Process (DANP), an integrated DEMATEL-ANP multi-criteria approach to evaluate and prioritize key resilience-related spatial and planning factors in TOD areas. Rather than using GIS flood modeling, we emphasize empirical indicators derived from local data, including urban density, transit accessibility, historical typhoon flood impacts, infrastructure vulnerability, and demographic exposure. An extensive literature review covers TOD principles, urban resilience theory, and DANP methodology, with a particular emphasis on the Taiwanese context and case studies. Empirical results reveal that specific TOD characteristics indeed enhance typhoon resilience. High-density, mixed-use development around transit can reduce overall exposure to hazards by curbing sprawl into floodplains and enabling efficient evacuations. Using DANP, we find that infrastructure robustness and emergency planning capacity emerge as the most influential factors for resilience in Taipei’s TOD neighborhoods, followed by land use and management and transit accessibility. Weighted rankings of Taipei’s districts suggest that centrally located TOD-intensive districts score higher in resilience metrics, while peripheral districts with flood-prone areas tend to lag. The Discussion explores these findings, considering planning policies—noting that TOD can bolster resilience if coupled with adaptive infrastructure and inclusive planning—and compares them with examples like Singapore’s integrated land use and transit strategy, which dramatically reduced flood risk. The study concludes with policy implications for integrating TOD and climate resilience in urban planning, and contributions of the DANP approach for complex urban resilience evaluations. Full article
(This article belongs to the Special Issue Urban Adaptation to Heat and Climate Change)
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24 pages, 3070 KB  
Article
Examining the Probabilistic Characteristics of Maximum Rainfall in Türkiye
by Ibrahim Temel, Omer Levend Asikoglu and Harun Alp
Atmosphere 2025, 16(10), 1177; https://doi.org/10.3390/atmos16101177 - 11 Oct 2025
Viewed by 200
Abstract
Hydrologists need to predict extreme hydrological and meteorological events for design purposes, whose magnitude and probability are estimated using a probability distribution function (PDF). The choice of an appropriate PDF is crucial in describing the behavior of the phenomenon and the predictions can [...] Read more.
Hydrologists need to predict extreme hydrological and meteorological events for design purposes, whose magnitude and probability are estimated using a probability distribution function (PDF). The choice of an appropriate PDF is crucial in describing the behavior of the phenomenon and the predictions can differ significantly depending on the PDF. So, the success of the probability distribution function in representing the data of extreme value series of natural events such as hydrology and climatology is of great importance. Depending on whether the series consists of maximum or minimum values, the theoretical probability density function must be appropriately fit to the right or left tail of the extreme data, which contains the most critical information. This study includes a combined evaluation of the performance of four different tests for selecting the appropriate probability distribution of maximum rainfall in Türkiye: Kolmogorov–Smirnov (KS) test, Anderson–Darling (AD) test, Probability Plot Correlation Coefficient (PPCC) test, and L-Moments ZDIST test. Within the scope of the study, maximum rainfall series of seven rainfall durations from 15 to 1440 min, at rain gauge stations in 81 provinces of Türkiye, were examined. Goodness of fit was performed based on ranking using a combination of four different numerical tests (KS, AD, PPCC, ZDIST). The probabilistic character of maximum rainfall was evaluated using a large dataset consisting of 567 time series with record lengths ranging from 45 to 80 years. The goodness of fit of distributions was examined from three different perspectives. The first is an examination considering rainfall durations, the second is a province-based examination, and the third is a general country-based assessment. In all three different perspectives, the Wakeby distribution was determined as the best fit candidate to represent the maximum rainfall in Türkiye. Full article
(This article belongs to the Section Meteorology)
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17 pages, 3396 KB  
Article
Determinants of Odor-Related Perception: Analysis of Community Response
by Franciele Ribeiro Cavalcante, Milena Machado, Valdério Anselmo Reisen, Bruno Furieri, Elisa Valentim Goulart, Antonio Ponce de Leon, Neyval Costa Reis, Jr., Séverine Frère and Jane Meri Santos
Atmosphere 2025, 16(10), 1176; https://doi.org/10.3390/atmos16101176 - 11 Oct 2025
Viewed by 185
Abstract
This study intends to identify and quantify the individual, perceptual, and contextual factors associated with odor-related perception and to assess the perception of odor sources according to meteorological conditions. Two face-to-face seasonal community surveys were conducted using stratified random sampling with proportional allocation, [...] Read more.
This study intends to identify and quantify the individual, perceptual, and contextual factors associated with odor-related perception and to assess the perception of odor sources according to meteorological conditions. Two face-to-face seasonal community surveys were conducted using stratified random sampling with proportional allocation, yielding representative samples of residents in a southern Brazilian city, where mild constant temperatures throughout the year and shifting prevailing wind directions expose residents to different odor sources. Chi-Square tests were applied to assess associations between odor perception and qualitative variables, while logistic regression was used to identify predictors of higher annoyance. Results showed that prevailing wind direction influenced source attribution, with steel industry and sewage-related sites most frequently cited. Proximity to the steel plant increased both source recognition and annoyance levels. Reported impacts included closing windows and reducing outdoor activities. Self-reported respiratory problems consistently predicted higher annoyance levels in both surveys. The statistical methods were effective in analyzing the likelihood of odor-related perception and its relationship with explanatory variables. These findings highlight the value of a data-driven approach—specifically, integrating wind direction, source proximity, and community-based perception—to support urban environmental management and guide odor mitigation strategies. Full article
(This article belongs to the Special Issue Atmospheric Pollutants: Monitoring and Observation (2nd Edition))
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14 pages, 4878 KB  
Article
Near-Surface Temperature Prediction Based on Dual-Attention-BiLSTM
by Wentao Xie, Mei Du, Chengbo Li and Guangxin Du
Atmosphere 2025, 16(10), 1175; https://doi.org/10.3390/atmos16101175 - 10 Oct 2025
Viewed by 208
Abstract
Current temperature prediction methods often focus on time-series information while neglecting the contributions of different meteorological factors and the context of varying time steps. Accordingly, this study developed a Dual-Attention-BiLSTM (a bidirectional long short-term memory network with dual attention mechanisms) network model, which [...] Read more.
Current temperature prediction methods often focus on time-series information while neglecting the contributions of different meteorological factors and the context of varying time steps. Accordingly, this study developed a Dual-Attention-BiLSTM (a bidirectional long short-term memory network with dual attention mechanisms) network model, which integrates a bidirectional long short-term memory (BiLSTM) network model with random forest-based feature selection and two self-designed attention mechanisms. A sensitivity analysis was conducted to evaluate the influence of the attention mechanisms. This study focuses on Shijiazhuang City, China, which has a temperate continental monsoon climate with significant seasonal and daily variations. The data were sourced from ERA5-Land, comprising hourly near-surface temperature and related meteorological variables for the year of 2022. The results indicate that integrating the two attention mechanisms significantly improves the model’s prediction performance compared to using BiLSTM alone. The mean absolute error between simulation results ranges from 0.80 °C to 1.08 °C, with a reduction of 0.17 °C to 0.39 °C, and the root mean square error ranges from 1.17 °C to 1.37 °C, with a reduction of 0.12 °C to 0.22 °C. Full article
(This article belongs to the Section Meteorology)
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21 pages, 5696 KB  
Review
Advancing Research on Urban Ecological Corridors in the Context of Carbon Neutrality: Insights from Bibliometric and Systematic Reviews
by Jing Li, Lang Zhang, Yang Yi and Jingbo Hong
Atmosphere 2025, 16(10), 1174; https://doi.org/10.3390/atmos16101174 - 10 Oct 2025
Viewed by 154
Abstract
The construction and maintenance of ecological corridors not only facilitate species migration and gene flow but also enhance ecosystem stability and resilience, providing critical support for achieving global carbon neutrality goals. Despite their importance, research on urban ecological corridors—specifically their role in carbon [...] Read more.
The construction and maintenance of ecological corridors not only facilitate species migration and gene flow but also enhance ecosystem stability and resilience, providing critical support for achieving global carbon neutrality goals. Despite their importance, research on urban ecological corridors—specifically their role in carbon sequestration and emission reduction within urban environments—remains insufficiently explored. To address this gap, we employed bibliometric and network analysis methods, utilizing the CiteSpace6.3.1 visualization tool to systematically review existing literature from the Web of Science Core Collection database. This study examines the research progress and trends in urban ecological corridors from 2000 to 2023, focusing on their role and significance in the context of global carbon neutrality. The findings reveal the following: (1) Research attention has grown steadily from 2000 to 2023, with climate change, carbon emission dynamics, and biodiversity emerging as core themes, reflecting increasing global focus on the carbon neutrality functions of urban ecological corridors. (2) CiteSpace analysis identified key research hotspots through keywords including climate change, carbon cycle, ecosystem services, model simulation, and ecological network analysis, revealing the functional mechanisms and pathways of urban ecological corridors in carbon neutrality contexts. (3) Current scientific challenges focus on understanding three core aspects of urban ecological corridors, the compositional elements, spatial structural design, and functional capacity assessment, requiring systematic theoretical breakthroughs. (4) Future research should prioritize exploring mechanisms to enhance urban ecological corridor functions and constructing low-carbon urban ecological networks, providing theoretical guidance and practical pathways for achieving urban emission reduction and climate goals. This study contributes to integrating research on the effectiveness of urban ecological corridors and carbon sinks, offering theoretical insights and practical guidance for reducing urban emissions and achieving climate goals. Full article
(This article belongs to the Special Issue Water Resource Challenges and Sustainable Management Solutions Under the Interaction of Climate Change and Human Activities)
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23 pages, 15077 KB  
Article
Landscape Patterns and Carbon Emissions in the Yangtze River Basin: Insights from Ensemble Models and Nighttime Light Data
by Banglong Pan, Qi Wang, Zhuo Diao, Jiayi Li, Wuyiming Liu, Qianfeng Gao, Ying Shu and Juan Du
Atmosphere 2025, 16(10), 1173; https://doi.org/10.3390/atmos16101173 - 9 Oct 2025
Viewed by 176
Abstract
Land use patterns are a critical driver of changes in carbon emissions, making it essential to elucidate the relationship between regional carbon emissions and land use types. As a nationally designated economic strategic zone, the Yangtze River Basin encompasses megacities, rapidly developing medium-sized [...] Read more.
Land use patterns are a critical driver of changes in carbon emissions, making it essential to elucidate the relationship between regional carbon emissions and land use types. As a nationally designated economic strategic zone, the Yangtze River Basin encompasses megacities, rapidly developing medium-sized cities, and relatively underdeveloped regions. However, the mechanism underlying the interaction between landscape patterns and carbon emissions across such gradients remains inadequately understood. This study utilizes nighttime light, land use and carbon emissions datasets, employing XGBoost, CatBoost, LightGBM and a stacking ensemble model to analyze the impacts and driving factors of land use changes on carbon emissions in the Yangtze River Basin from 2002 to 2022. The results showed: (1) The stacking ensemble learning model demonstrated the best predictive performance, with a coefficient of determination (R2) of 0.80, a residual prediction deviation (RPD) of 2.22, and a root mean square error (RMSE) of 4.46. Compared with the next-best models, these performance metrics represent improvements of 19.40% in R2 and 28.32% in RPD, and a 22.16% reduction in RMSE. (2) Based on SHAP feature importance and Pearson correlation analysis, the primary drivers influencing CO2 net emissions in the Yangtze River Basin are GDP per capita (GDPpc), population density (POD), Tertiary industry share (TI), land use degree comprehensive index (LUI), dynamic degree of water-body land use (Kwater), Largest patch index (LPI), and number of patches (NP). These findings indicate that changes in regional landscape patterns exert a significant effect on carbon emissions in strategic economic regions, and that stacked ensemble models can effectively simulate and interpret this relationship with high predictive accuracy, thereby providing decision support for regional low-carbon development planning. Full article
(This article belongs to the Special Issue Urban Carbon Emissions: Measurement and Modeling)
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17 pages, 7446 KB  
Article
Seasonal Cycle of the Total Ozone Content over Southern High Latitudes in the CCM SOCOLv3
by Anastasia Imanova, Tatiana Egorova, Vladimir Zubov, Andrey Mironov, Alexander Polyakov, Georgiy Nerobelov and Eugene Rozanov
Atmosphere 2025, 16(10), 1172; https://doi.org/10.3390/atmos16101172 - 9 Oct 2025
Viewed by 218
Abstract
The severe ozone depletion over the Southern polar region, known as the “ozone hole,” is a stark example of global ozone depletion caused by human-made chemicals. This has implications for climate change and increased harmful surface solar UV. Several Chemistry–Climate models (CCMs) tend [...] Read more.
The severe ozone depletion over the Southern polar region, known as the “ozone hole,” is a stark example of global ozone depletion caused by human-made chemicals. This has implications for climate change and increased harmful surface solar UV. Several Chemistry–Climate models (CCMs) tend to underestimate total column ozone (TCO) against satellite measurements over the Southern polar region. This underestimation can reach up to 50% in monthly mean zonally averaged biases during cold seasons. The most significant discrepancies were found in the CCM SOlar Climate Ozone Links version 3 (SOCOLv3). We use SOCOLv3 to study the sensitivity of Antarctic TCO to three key factors: (1) stratospheric heterogeneous reaction efficiency, (2) meridional flux intensity into polar regions from sub-grid scale mixing, and (3) photodissociation rate calculation accuracy. We compared the model results with satellite data from Infrared Fourier Spectrometer-2 (IKFS-2), Microwave Limb Sounder (MLS), and Michelson Interferometer for Passive Atmospheric Sounding (MIPAS). The most effective processes for improving polar ozone simulation are photolysis and horizontal mixing. Increasing horizontal mixing improves the simulated TCO seasonal cycle but negatively impacts CH4 and N2O distributions. Using the Cloud-J v.8.0 photolysis module has improved photolysis rate calculations and the seasonal ozone cycle representation over the Southern polar region. This paper outlines how different processes impact chemistry–climate model performance in the southern polar stratosphere, with potential implications for future advancements. Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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26 pages, 4574 KB  
Review
Assessment of Climate Vulnerability Indices for Coastal Tourism Destinations
by Beatriz Gasalla-López, Manuel Arcila-Garrido and Juan Adolfo Chica-Ruiz
Atmosphere 2025, 16(10), 1171; https://doi.org/10.3390/atmos16101171 - 9 Oct 2025
Viewed by 159
Abstract
Coastal ecosystems are crucial for territorial development but they face increasing pressure from population growth and climate change. These factors threaten ecosystems, communities, and tourism infrastructure. It is essential to assess vulnerability to achieve adaptation and indices are widely used for this purpose [...] Read more.
Coastal ecosystems are crucial for territorial development but they face increasing pressure from population growth and climate change. These factors threaten ecosystems, communities, and tourism infrastructure. It is essential to assess vulnerability to achieve adaptation and indices are widely used for this purpose due to their simplicity. However, inconsistencies persist in definitions, methodologies, dimensions, and variable selection. This systematic review of 43 second-generation studies analyzes the evolution of conceptual approaches, identifies the most common indicators, and examines index methodologies. The results reveal that, although the IPCC has updated its definition of vulnerability, many publications still use previous conceptual frameworks. While temperature is relevant to tourism, most studies focus on increasing sea level and its effects. In some cases, social and economic dimensions are treated jointly whereas in other studies they are considered separately. Variable selection remains case-specific and a robust, standardized framework is still lacking, especially for social aspects. Despite the undoubted importance of tourism, specific research on this sector is scarce. This review underscores the need for standardized indices tailored to coastal tourism management under climate change. Future research directions are also proposed. Full article
(This article belongs to the Section Biosphere/Hydrosphere/Land–Atmosphere Interactions)
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24 pages, 2315 KB  
Article
Mitigating Climate Warming: Mechanisms and Actions
by Jianhui Bai, Xiaowei Wan, Angelo Lupi, Xuemei Zong and Erhan Arslan
Atmosphere 2025, 16(10), 1170; https://doi.org/10.3390/atmos16101170 - 9 Oct 2025
Viewed by 213
Abstract
To validate a positive relationship between air temperature (T) and atmospheric substances (S/G, a ratio of diffuse solar radiation to global solar radiation) found at four typical stations on the Earth, and a further investigation was conducted. Based on the analysis of long-term [...] Read more.
To validate a positive relationship between air temperature (T) and atmospheric substances (S/G, a ratio of diffuse solar radiation to global solar radiation) found at four typical stations on the Earth, and a further investigation was conducted. Based on the analysis of long-term solar radiation, atmospheric substances, and air temperature at 29 representative stations of baseline surface radiation network (BSRN) in the world, the relationships and the mechanisms between air temperature and atmospheric substances were studied in more detail. A universal non-linear relationship between T and S/G was still found, which supported the previous relationship between T and S/G. This further revealed that a high (or low) air temperature is strongly associated with large (or small) amounts of atmospheric substances. The mechanism is that all kinds of atmospheric substances can keep and accumulate solar energy in the atmosphere and then heat the atmosphere, causing atmospheric warming at the regional and global scales. Therefore, it is suggested to reduce the direct emissions of all kinds of atmospheric substances (in terms gases, liquids and particles, and GLPs) from the natural and anthropogenic sources, and secondary formations produced from atmospheric compositions via chemical and photochemical reactions (CPRs) in the atmosphere, to slow down the regional and global warming through our collective efforts, by all mankind and all nations. Air temperature increased at most BSRN stations and many sites in China, and decreased at a small number of BSRN stations during long time scales, revealing that the mechanisms of air temperature change were very complex and varied with region, atmospheric substances, and the interactions between solar radiation, GLPs, and the land. Full article
(This article belongs to the Section Biosphere/Hydrosphere/Land–Atmosphere Interactions)
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12 pages, 1521 KB  
Article
Investigation and Analysis of Indoor Radon Concentrations in Typical Residential Areas in Central China
by Cong Li, Jun Deng, Gangtao Sun, Fang Wang, Jie Yu, Qi Xiao, Shi Liu and Wenshan Zhou
Atmosphere 2025, 16(10), 1169; https://doi.org/10.3390/atmos16101169 - 9 Oct 2025
Viewed by 199
Abstract
In recent years, China has experienced a notable increase in indoor radon concentrations. However, our understanding of residential radon exposure in Central China remains limited and primarily depends on the data collected from residential buildings in Wuhan before 2003. Given this context, the [...] Read more.
In recent years, China has experienced a notable increase in indoor radon concentrations. However, our understanding of residential radon exposure in Central China remains limited and primarily depends on the data collected from residential buildings in Wuhan before 2003. Given this context, the current radon exposure levels in Central China must be assessed immediately, and the factors influencing them be investigated. To address this gap, our study focused on five representative areas in Central China. We monitored indoor radon concentrations in residential areas using random cluster sampling while considering various building structures. The radon levels were measured through the alpha track method, and RSKS standard detectors were deployed in two separate batches to participating households. A total of 1300 detectors were distributed across 579 households, with a recovery rate of 97.15% (1263 detectors were retrieved). The annual average indoor radon concentration in Central China ranged widely from 6.25 Bq/m3 to 310.44 Bq/m3, with an arithmetic mean of 50.20 Bq/m3, which resulted in an average annual effective dose of 2.08 mSv. Referring to World Health Organization standards, the radon concentration in approximately 8.24% of the monitored rooms exceeded the recommended action level. Our analysis indicated that radon concentration is primarily influenced by factors, such as the time of measurement, geographical location, building structure, floor materials, household fuel, and ventilation practices. Multiple regression analysis revealed that these factors collectively account for 10.80% of the variation in radon concentration. Notably, geographical location, building structure, and ventilation mode emerged as important factors. Based on these findings, our study suggests several practical measures to effectively reduce indoor radon levels, including improving ventilation, switching to cleaner fuels, and using environmentally friendly building and decoration materials. Full article
(This article belongs to the Special Issue Environmental Radon Measurement and Radiation Exposure Assessment)
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19 pages, 7633 KB  
Article
A Transfer Learning–CNN Framework for Marine Atmospheric Pollutant Inversion Using Multi-Source Data Fusion
by Xiaoling Li, Xiaoyu Liu, Xiaohuan Liu, Zhengyang Zhu, Yunhui Xiong, Jingfei Hu and Xiang Gong
Atmosphere 2025, 16(10), 1168; https://doi.org/10.3390/atmos16101168 - 8 Oct 2025
Viewed by 278
Abstract
The concentration characteristics of SO2, NO2, O3, and CO in the marine atmosphere are of great significance for understanding air–sea interactions and regional atmospheric chemical processes. However, due to the challenging conditions of marine monitoring, long-term continuous [...] Read more.
The concentration characteristics of SO2, NO2, O3, and CO in the marine atmosphere are of great significance for understanding air–sea interactions and regional atmospheric chemical processes. However, due to the challenging conditions of marine monitoring, long-term continuous observational data remain scarce. To address this gap, this study proposes a Transfer Learning–Convolutional Neural Network (TL-CNN) model that integrates ERA5 meteorological data, EAC4 atmospheric composition reanalysis data, and ground-based observations through multi-source data fusion. During data preprocessing, the Data Interpolating Empirical Orthogonal Function (DINEOF), inverse distance weighting (IDW) spatial interpolation, and Gaussian filtering methods were employed to improve data continuity and consistency. Using ERA5 meteorological variables as inputs and EAC4 pollutant concentrations as training targets, a CNN-based inversion framework was constructed. Results show that the CNN model achieved an average coefficient of determination (R2) exceeding 0.80 on the pretraining test set, significantly outperforming random forest and deep neural networks, particularly in reproducing nearshore gradients and regional spatial distributions. After incorporating transfer learning and fine-tuning with station observations, the model inversion results reached an average R2 of 0.72 against site measurements, effectively correcting systematic biases in the reanalysis data. Among the pollutants, the inversion of SO2 performed relatively poorly, mainly because emission reduction trends from anthropogenic sources were not sufficiently represented in the reanalysis dataset. Overall, the TL-CNN model provides more accurate pollutant concentration fields for offshore regions with limited observations, offering strong support for marine atmospheric environment studies and assessments of marine ecological effects. It also demonstrates the potential of combining deep learning and transfer learning in atmospheric chemistry research. Full article
(This article belongs to the Section Aerosols)
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13 pages, 1968 KB  
Article
Assessing the Annual-Scale Insolation–Temperature Relationship over Northern Hemisphere in CMIP6 Models and Its Implication for Orbital-Scale Simulation
by Shengmei Li and Jian Shi
Atmosphere 2025, 16(10), 1167; https://doi.org/10.3390/atmos16101167 - 8 Oct 2025
Viewed by 242
Abstract
Previous studies have suggested that Earth’s annual cycle of modern climate provides information relevant to orbital-scale climate variability, since both are driven by solar insolation changes determined by orbital geometry. However, there has been no systematic assessment of the climate response to annual-scale [...] Read more.
Previous studies have suggested that Earth’s annual cycle of modern climate provides information relevant to orbital-scale climate variability, since both are driven by solar insolation changes determined by orbital geometry. However, there has been no systematic assessment of the climate response to annual-scale insolation changes in climate models, leading to large uncertainty in orbital-scale simulation. In this study, we evaluate the Northern Hemisphere land surface air temperature response to the annual insolation cycle in the Coupled Model Intercomparison Project Phase 6 (CMIP6) models. A polynomial transfer framework reveals that CMIP6 models broadly capture the observed 20–30-day lag between insolation and temperature, indicating realistic land thermal inertia. However, CMIP6 models consistently overestimate temperature sensitivities to insolation, with particularly strong biases over mid-latitude and high-latitude regions in summer and winter, respectively. Applying the annual-scale polynomial transfer framework to the middle Holocene (~6000 years ago) shows that models with the highest sensitivity simulate significantly larger seasonal temperature anomalies than the lowest-sensitivity models, underscoring the impact of modern biases on orbital-scale paleoclimate simulations. The results highlight systematic overestimation of temperature–insolation sensitivity in CMIP6 models, emphasizing the importance of constraining seasonal sensitivity for robust orbital-scale climate modeling. Full article
(This article belongs to the Section Climatology)
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