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Volume 16
Issue 11
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Atmosphere, Volume 16, Issue 11 (November 2025) – 92 articles

Cover Story (view full-size image): A prototype moss-based green roof was developed using a newly cultivated strain of Racomitrium japonicum, Dozy & Molk., optimized for harsh rooftop environments. Field evaluations on a building rooftop showed that the moss maintained healthy growth even during summer heat-wave conditions. The system effectively reduced rooftop surface temperatures by up to 20 °C and stored approximately 0.3 kg C/m2 per year. These findings highlight the potential of moss-based green roofs as a practical solution for mitigating heat-island effects and enhancing carbon sequestration in urban areas, offering advantages beyond those of conventional green roof systems, especially their lightweight structure and low maintenance. View this paper
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40 pages, 7970 KB  
Review
Review of Subionospheric VLF/LF Radio Signals for the Study of Seismogenic Lower-Ionospheric Perturbations
by Masashi Hayakawa
Atmosphere 2025, 16(11), 1312; https://doi.org/10.3390/atmos16111312 - 20 Nov 2025
Viewed by 599
Abstract
It has recently been recognized that the ionosphere is highly sensitive to pre-seismic effects, and the detection of ionospheric perturbations associated with earthquakes (EQs) is one of the most promising candidates for short-term EQ prediction. In this review, we focus on a possible [...] Read more.
It has recently been recognized that the ionosphere is highly sensitive to pre-seismic effects, and the detection of ionospheric perturbations associated with earthquakes (EQs) is one of the most promising candidates for short-term EQ prediction. In this review, we focus on a possible use of VLF/LF (very low frequency (3–30 kHz)/low frequency (30–300 kHz)) radio sounding of seismo-ionospheric perturbations to study seismogenic effects. Because an understanding of the early history in any area will provide a lot of crucial insights to the readers (especially to young scientists) working in the field of seismo-electromagnetics, we provide a brief history (mainly results reported by a Russian group of scientists) of the initial application of subionospheric VLF/LF propagation for the study of ionospheric perturbations associated with EQs, and then we present our first convincing evidence on the ionospheric perturbation for the disastrous Kobe EQ in 1995, with a new analysis method based on the shifts in terminator times in VLF/LF diurnal variations (minima in the diurnal variations in amplitude and phase). We then summarize our latest results on further evidence of seismo-ionospheric perturbations. Firstly, we present a few statistical studies on the correlation between VLF/LF propagation anomalies and EQs based on long-term data. Secondly, we showcase studies for a few large, recent EQs (including the 2011 Tohoku EQ). Building on those EQ precursor studies, we demonstrate scientific topics and the underlying physics that can be studied using VLF/LF data, highlighting recent achievements including the revolutionary perspective of lithosphere–atmosphere–ionosphere coupling (LAIC) (or how the ionosphere is perturbed due to the lithospheric pre-EQ activity), modulation in VLF/LF data by atmospheric gravity waves (AGWs), Doppler-shift observation, satellite observation of VLF/LF transmitter signals, etc., together with the recommendation of the application of new technologies such as artificial intelligence and critical analysis to VLF/LF analysis. Finally, we want to emphasize again the essential significance of the information on lower-ionospheric perturbations within LAIC studies. Full article
(This article belongs to the Special Issue Understanding Space Physics and Atmospheric Electricity with VLF/ELF Signals)
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15 pages, 3422 KB  
Article
Comparative Study of Atmospheric Polycyclic Aromatic Hydrocarbons (PAHs) and Nitro-PAHs at Marine and Forest Background Stations in Shimane, Japan (2022–2024)
by Yan Wang, Pengchu Bai, Xuan Zhang, Shingo Matsumoto, Tamon Yamashita, Masa-aki Yoshida, Seiya Nagao, Ammara Habib, Bushra Khalid, Lulu Zhang, Bin Chen and Ning Tang
Atmosphere 2025, 16(11), 1311; https://doi.org/10.3390/atmos16111311 - 20 Nov 2025
Viewed by 300
Abstract
To clarify the pollution characteristics of polycyclic aromatic hydrocarbons (PAHs) and nitro-PAHs (NPAHs) in the East Asian monsoon region under different atmospheric environments and to assess their potential influences on receptor areas, this study selected two background monitoring stations with different environments in [...] Read more.
To clarify the pollution characteristics of polycyclic aromatic hydrocarbons (PAHs) and nitro-PAHs (NPAHs) in the East Asian monsoon region under different atmospheric environments and to assess their potential influences on receptor areas, this study selected two background monitoring stations with different environments in Shimane Prefecture, Japan: a marine station (MB) and a forest station (SF). PM2.5 samples were simultaneously collected using a high-volume sampler during the summer and winter of 2022–2023, and ten PAHs and three NPAHs were quantified using HPLC. The concentrations of PAHs and NPAHs at MB and SF exhibited significant seasonal variations in 2022 (winter > summer). However, in 2023, a clear seasonal difference was observed only at MB. Isomer ratio analysis of PAHs at both stations indicated that traffic emissions and biomass or coal combustion were major contributors. Seasonal variations in the [2-NFR]/[1-NP] ratio indicated that, while high ratios at MB and SF during summer were mainly associated with local photochemical formation, low ratios in winter reflected long-range transportation of combustion-derived PAHs and NPAHs from the Asian continent. Incremental lifetime cancer risk values (10−7 to 10−11) indicated that even at background stations, the atmospheric environment poses certain health risks. This first comparative investigation of PAHs and NPAHs at two distinct background stations in Shimane again highlights the importance of international cooperation among East Asian countries for effective air pollution control. Full article
(This article belongs to the Section Air Quality and Health)
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20 pages, 3702 KB  
Article
Indications of the Impact of the Influence of Large-Scale Atmospheric Disturbances on Quasiperiodic ELF/VLF Emissions Inside the Plasmasphere
by Peter Bespalov, Olga Savina and Polina Shkareva
Atmosphere 2025, 16(11), 1310; https://doi.org/10.3390/atmos16111310 - 20 Nov 2025
Viewed by 214
Abstract
The models of excitation of quasiperiodic ELF/VLF emissions with spectral shape repetition periods from 10 to 300 s are discussed. The primary cause of quasiperiodic (QP) emissions is cyclotron instability of electron radiation belts. Relatively slow processes of cyclotron instability evolution are well [...] Read more.
The models of excitation of quasiperiodic ELF/VLF emissions with spectral shape repetition periods from 10 to 300 s are discussed. The primary cause of quasiperiodic (QP) emissions is cyclotron instability of electron radiation belts. Relatively slow processes of cyclotron instability evolution are well described within the framework of the plasma magnetospheric maser (PMM) theory based on the averaged self-consistent system of quasilinear equations for particles and waves. The presence of an eigen-frequency of oscillations of PMM parameters allows explaining many properties of QP 1 emissions, in which not very clear spectral bursts are hiss with resonant modulation mainly near the upper spectral boundary by geomagnetic pulsations of the Pc 3–4 range. The analysis of the general problem of equilibrium of radiation belts shows the possibility of its instability, which is caused by the difference in the pitch-angle dependences of the particle source power and the steady state distribution function. In the nonlinear mode of the specified instability, QP 2 emissions are formed, often with an increase in frequencies in individual spectral bursts. This paper mainly focuses on the study of QP 2 emissions with both a normal and an atypical time structure, as well as with large and fast dynamics of the frequency spectrum. Periodic large-scale atmospheric disturbances with a suitable frequency on the ionosphere can significantly affect the operating modes of the PMM and, as a consequence, the quasiperiodic VLF emissions in the magnetosphere. Infrasonic waves at the altitudes of the E region of the ionosphere can provide excitation of atypical quasiperiodic emissions due to a change in the reflection coefficient of whistler waves from the ionosphere from above. The obtained results are important for interpreting observational data on emissions associated with large-scale processes in the atmosphere. To analyze the magnetosphere response to earthquakes, observation data from the Van Allen Probe spacecraft were used. Also, specific examples of quasiperiodic emissions, probably associated with large-scale atmospheric processes, were obtained during the analysis of observational data. Full article
(This article belongs to the Special Issue Understanding Space Physics and Atmospheric Electricity with VLF/ELF Signals)
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29 pages, 1134 KB  
Review
Particle Size as a Key Driver of Black Carbon Wet Removal: Advances and Insights
by Yumeng Qiao, Jiajia Wang, Li Wang and Baiqing Xu
Atmosphere 2025, 16(11), 1309; https://doi.org/10.3390/atmos16111309 - 20 Nov 2025
Viewed by 537
Abstract
Black carbon (BC), as a potent light-absorbing aerosol, is mainly removed from the atmosphere through wet deposition. The efficiency of this process depends on the capacity of BC particles to serve as cloud condensation nuclei (CCN) or ice nuclei (IN). Newly emitted BC [...] Read more.
Black carbon (BC), as a potent light-absorbing aerosol, is mainly removed from the atmosphere through wet deposition. The efficiency of this process depends on the capacity of BC particles to serve as cloud condensation nuclei (CCN) or ice nuclei (IN). Newly emitted BC particles are typically small in size and highly hydrophobic, which limits their activation potential. However, atmospheric aging processes involving interactions with sulfates, nitrates, or organic matter enhance their hydrophilicity and nucleation capacity. Particle size serves as the critical link between aging and removal processes. Larger or coated BC particles are more readily activated and removed, while smaller particles require higher supersaturation levels. Both observations and models indicate that uncertainties in BC particle size distribution and aging processes lead to significant discrepancies in lifetime and transport estimates. This paper reviews recent research on the size dependence of wet removal of BC, evaluates current observational and modeling results, and proposes key research priorities to more accurately constrain its role in the climate system. Full article
(This article belongs to the Section Air Pollution Control)
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22 pages, 1627 KB  
Article
Multiscale Variability of Atmospheric CO2 at the Azores: Detecting Seasonal and Decadal Oscillations
by Maria Gabriela Meirelles and Helena Cristina Vasconcelos
Atmosphere 2025, 16(11), 1308; https://doi.org/10.3390/atmos16111308 - 20 Nov 2025
Viewed by 281
Abstract
Atmospheric carbon dioxide (CO2) levels are rising globally, yet their multiscale variability in remote oceanic regions remains poorly characterized. This study examines a 45-year monthly CO2 record (1980–2024) from the Azores, a subtropical North Atlantic site, using a spectral and [...] Read more.
Atmospheric carbon dioxide (CO2) levels are rising globally, yet their multiscale variability in remote oceanic regions remains poorly characterized. This study examines a 45-year monthly CO2 record (1980–2024) from the Azores, a subtropical North Atlantic site, using a spectral and statistical framework. The series was decomposed into high- and low-frequency components via Butterworth filtering and analyzed with the Correlogram-Based Periodogram (CBP) and Monte Carlo significance testing. The residual component robustly recovered the expected seasonal cycle (~12 months), validating the methodology. The trend component revealed an apparent enhancement in low-frequency spectral power, largely explained by the accelerating long-term increase. Control tests with a synthetic quadratic trend and polynomial detrending indicate a weak ~11-year enhancement in low-frequency power that is not robust under a red-noise (AR(1)) null. Segmented regressions showed a sustained and accelerating increase in CO2 accumulation over the past four decades, consistent with Mauna Loa. These results demonstrate the importance of long-term monitoring in remote regions while highlighting both the potential and limitations of spectral methods for detecting weak low-frequency signals in greenhouse gas records. Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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21 pages, 6345 KB  
Article
Climate Impact on the Seasonal and Interannual Variation in NDVI and GPP in Mongolia
by Justinas Kilpys, Egidijus Rimkus, Oyunsanaa Byambasuren, Jambajamts Lkhamjav and Tseren-Ochir Soyol-Erdene
Atmosphere 2025, 16(11), 1307; https://doi.org/10.3390/atmos16111307 - 19 Nov 2025
Viewed by 335
Abstract
This study examined the influence of climate variability on vegetation dynamics in Mongolia from 2000 to 2024, using ERA5-Land reanalysis data together with the Normalized Difference Vegetation Index (NDVI) and Gross Primary Productivity (GPP) indicators. The results show a statistically significant mean annual [...] Read more.
This study examined the influence of climate variability on vegetation dynamics in Mongolia from 2000 to 2024, using ERA5-Land reanalysis data together with the Normalized Difference Vegetation Index (NDVI) and Gross Primary Productivity (GPP) indicators. The results show a statistically significant mean annual air temperature increase of 0.94 °C, with the most pronounced warming occurring in March (>1.5 °C/10 years). Annual precipitation increased by 32 mm (~13%), mainly in the northern and eastern regions. At the same time, the maximum NDVI increased at a rate of 0.025 units/10 years, particularly in the north and east, while no change or slight decline was observed in the central steppes during May–June. During the study period, the average annual GPP increased by 38%, from 0.25 to 0.35 kgCm−2, with the highest gains observed in northern forests and eastern steppes. Correlation analysis revealed that NDVI is most sensitive to temperature in early spring (r = 0.31) and to precipitation in summer (r = 0.45–0.50). GPP primarily is driven by temperature in spring (r = 0.68) and by precipitation during summer (r = 0.30). The results of this study indicate that vegetation productivity in Mongolia is sensitive to seasonal climate variability, with temperature being the primary factor influencing spring growth and precipitation controlling summer growth. Full article
(This article belongs to the Section Climatology)
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11 pages, 1515 KB  
Article
Estimation of Global Solar Radiation in Unmonitored Areas of Brazil Using ERA5 Reanalysis and Artificial Neural Networks
by Eduardo Morgan Uliana, Juliana de Abreu Araujo, Márcio Roggia Zanuzo, Alvaro Henrique Guedes Araujo, Marionei Fomaca de Sousa Junior, Uilson Ricardo Venâncio Aires and Herval Alves Ramos Filho
Atmosphere 2025, 16(11), 1306; https://doi.org/10.3390/atmos16111306 - 19 Nov 2025
Viewed by 362
Abstract
Estimating global radiation (GR) is crucial for assessing solar energy potential, understanding surface energy balance, and forecasting agricultural production. However, several regions require additional monitoring and sparse sensor networks. The ERA5-ECMWF reanalysis is a viable alternative for estimating meteorological elements in unmonitored areas. [...] Read more.
Estimating global radiation (GR) is crucial for assessing solar energy potential, understanding surface energy balance, and forecasting agricultural production. However, several regions require additional monitoring and sparse sensor networks. The ERA5-ECMWF reanalysis is a viable alternative for estimating meteorological elements in unmonitored areas. This study aimed to train an artificial neural network (ANN) model to estimate GR based on ERA5 data and map its distribution in the study area. We utilized GR data from 32 automatic weather stations of the Brazilian National Institute of Meteorology in Mato Grosso, Brazil, for model training. The model input consisted of ERA5 air temperature, precipitation data, and top-of-atmosphere solar radiation (R0) calculated from the latitude and day of the year. The calibrated model demonstrated high accuracy, with Nash–Sutcliffe and Kling–Gupta efficiency indices exceeding 0.99. This enabled the generation of historical time series and maps of GR spatial distribution in the study area. The results demonstrate that—as input for the ANN—ERA5 data enables precise and accurate estimation of GR distribution, even in locations without meteorological stations. Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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18 pages, 2530 KB  
Article
Impacts of Climate Change on Rice Production in Pakistan: A Perspective from a Deep Learning Approach
by Muhammad Haroon Shah, Wilayat Shah, Sidra Syed, Irfan Ullah, Yaoyao Wang and Yuanyuan Wang
Atmosphere 2025, 16(11), 1305; https://doi.org/10.3390/atmos16111305 - 19 Nov 2025
Viewed by 452
Abstract
Ensuring food security in Pakistan, particularly for rice production, is a critical challenge due to increasing population demands and the growing impact of climate change variability. Accurate estimation of rice crop yields is essential for optimizing resource allocation, managing supply chains, and forecasting [...] Read more.
Ensuring food security in Pakistan, particularly for rice production, is a critical challenge due to increasing population demands and the growing impact of climate change variability. Accurate estimation of rice crop yields is essential for optimizing resource allocation, managing supply chains, and forecasting economic growth while minimizing agricultural losses. This study utilizes a Deep Neural Network (DNN) to predict rice yields in Pakistan by analyzing the effects of maximum temperature and precipitation trends under high-emission scenarios (SSP5-8.5) derived from CMIP6 climate models. Historical (1980–2014) and future (2015–2100) climate projections were evaluated using key variables, including precipitation, meteorological conditions, cultivated area, and crop yields. Results from CMIP6 SSP5-8.5 indicate a significant rise in maximum temperatures and increased precipitation variability, exacerbating risks to rice crop yields. DNN demonstrated superior accuracy in forecasting these trends, achieving high R-squared values and low error metrics, including Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The findings reveal that Pakistan, particularly Eastern South Asia, is highly vulnerable to climate extremes, with severe implications for rice production and agricultural sustainability. These results highlight the urgent need for policymakers to adopt climate adaptation strategies, including advanced predictive modeling and resilient agricultural practices, to safeguard rice production and ensure long-term food security in Pakistan’s monsoon-dependent regions. This study aligns with Sustainable Development Goal 2 (Zero Hunger) by contributing to food security and sustainable agricultural development, and with Sustainable Development Goal 13 (Climate Action) by addressing climate change impacts on agriculture and promoting resilience in rice production systems. Full article
(This article belongs to the Special Issue New Insights into Land–Atmosphere Interactions in Climate Dynamics)
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31 pages, 7085 KB  
Article
Integration of WRF-Chem Model-Based, Satellite-Based, and Ground-Based Observation Data to Predict PM2.5 Concentration by Machine Learning Approach
by Soottida Chimla, Chakrit Chotamonsak and Tawee Chaipimonplin
Atmosphere 2025, 16(11), 1304; https://doi.org/10.3390/atmos16111304 - 19 Nov 2025
Viewed by 501
Abstract
Fine particulate matter (PM2.5) is a critical environmental and health concern in northern Thailand, where haze episodes are strongly influenced by biomass burning, meteorological variability, and complex topography. This study aims to (1) analyze and select input variables for PM2.5 prediction by integrating [...] Read more.
Fine particulate matter (PM2.5) is a critical environmental and health concern in northern Thailand, where haze episodes are strongly influenced by biomass burning, meteorological variability, and complex topography. This study aims to (1) analyze and select input variables for PM2.5 prediction by integrating WRF-Chem outputs, satellite data, and ground observations, and (2) evaluate the predictive performance of four machine learning (ML) algorithms—Random Forest (RF), XGBoost, CNN3D, and ConvLSTM—during the 2024 haze season (January–May). The dataset included hourly PM2.5 observations from 54 stations, the WRF-Chem-simulated PM2.5 and meteorological variables, satellite-based fire data, and geographical data. To improve consistency with ground-based data, WRF-Chem PM2.5 values were bias-corrected for the training and validation phases prior to ML learning. Among Linear Regression, RF, XGBoost, Artificial Neural Network (ANN), and Convolutional Neural Network (CNN) tested for bias correction, RF achieved the best performance (R = 0.78, RMSE = 29.28 µg/m3); the RF-corrected WRF-Chem PM2.5 was then used as an input to the forecasting stage. Variable selection was supported by correlation, VIF, feature importance, and SHAP analyses. The results indicate that RF provided the most reliable predictions, achieving a correlation of R = 0.867 and the lowest RMSE of 27.6 µg/m3 when using the SHAP+VIF-selected input set (seven variables: PM2.5_lag1, PM2.5_lag24, T2, RH2, Precip, Burned Area, NDVI). Notably, RF remained the top performer, predicting PM2.5 more accurately than the other algorithms during high-pollution conditions, specifically Air Quality Index (AQI) “Unhealthy for Sensitive Groups” (high) and “Unhealthy” (very high). Taken together, RF set the performance bar across both stages, with XGBoost ranked second, whereas CNN3D and ConvLSTM performed considerably worse. These findings emphasize the effectiveness of ensemble tree-based algorithms combined with bias-corrected WRF-Chem outputs and strategic variable selection in supporting accurate hourly PM2.5 predictions for air quality management in biomass burning regions. Full article
(This article belongs to the Special Issue Dispersion and Mitigation of Atmospheric Pollutants)
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20 pages, 14159 KB  
Article
Mapping Invisible Risk: A Low-Cost Strategy for Identifying Air and Noise Pollution in Latin American Cities
by Lucas Ezequiel Romero Cortés, Iván Tavera Busso, Gabriela Alejandra Abril, Matías Ezequiel Reinaudi, Hebe Alejandra Carreras and Ana Carolina Mateos
Atmosphere 2025, 16(11), 1303; https://doi.org/10.3390/atmos16111303 - 18 Nov 2025
Cited by 1 | Viewed by 289
Abstract
Urban populations in Latin America are highly exposed to traffic-related pollutants, yet monitoring networks remain limited. This study proposes a low-cost methodology to identify urban pollution hotspots in the city of Córdoba, Argentina, by categorizing 20 sites based on traffic categories using Google [...] Read more.
Urban populations in Latin America are highly exposed to traffic-related pollutants, yet monitoring networks remain limited. This study proposes a low-cost methodology to identify urban pollution hotspots in the city of Córdoba, Argentina, by categorizing 20 sites based on traffic categories using Google Traffic data. Measurements of PM2.5, polycyclic aromatic hydrocarbons (PAHs), and equivalent sound pressure level (LAeq) were conducted over a 21-day cold-season period. Mean PM2.5 concentrations ranged from 7.5 to 27.3 µg/m3, and total PAHs ranged from 1.4 to 7.9 ng/m3. Sites with high and medium traffic density exhibited significantly higher PAH concentrations and noise levels, with LAeq5 values exceeding 65 dB at all urban core locations. Conversely, PM2.5 concentrations were higher at peripheral sites due to topography, dust resuspension, and wildfire events. Strong correlations were found between vehicular flow and noise (r = 0.94), and between heavy-vehicle proportion and noise (r = 0.60). The lifetime lung cancer risk associated with PAH exposure was classified as “low” according to USEPA criteria. This traffic-based categorization approach provides a rapid and cost-effective tool for identifying high-risk areas in resource-limited settings, supporting urban planning and public health interventions. Full article
(This article belongs to the Special Issue Urban Air Pollution and Traffic-Related Contaminants: Sources, Impacts and Challenges)
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18 pages, 6225 KB  
Article
Scattering Characteristics of Submicron Particulate Chemical Components During Winter in Northern and Southern Chinese Cities
by Jialin Shi, Mingzhe Li, Qinghong Wang, Wenfei Zhu, Liping Qiao, Shengrong Lou and Song Guo
Atmosphere 2025, 16(11), 1302; https://doi.org/10.3390/atmos16111302 - 18 Nov 2025
Viewed by 276
Abstract
Understanding aerosol chemical components’ roles in light extinction is critical for air quality management and climate mitigation. This study compared PM1 optical properties and chemical compositions in Shanghai (southern China) and Dezhou (northern China) during winter using high-resolution aerosol mass spectrometers and [...] Read more.
Understanding aerosol chemical components’ roles in light extinction is critical for air quality management and climate mitigation. This study compared PM1 optical properties and chemical compositions in Shanghai (southern China) and Dezhou (northern China) during winter using high-resolution aerosol mass spectrometers and optical instruments. Results showed PM1 scattering coefficients (10.9–549.8 Mm−1) in Shanghai were dominated by traffic-related organic aerosols (OA) (45.2%), with ammonium sulfate and nitrate contributing 60.5% of extinction. In Dezhou, higher scattering coefficients (3.5–2635.1 Mm−1) were driven by heating/biomass burning, with OA accounting for 57.8% and ammonium nitrate 27.2%. Mass scattering efficiencies (MSEs) in Dezhou were significantly higher (sulfate: 10.75 m2/g; nitrate: 10.15 m2/g; OA: 4.9 m2/g) than those in Shanghai (4.2/3.85/3.00 m2/g). Pollution episodes revealed distinct mechanisms: high-humidity OA accumulation for Shanghai vs. nitrate-organic synergy for Dezhou. The IMPROVE model systematically underestimated scattering coefficients, emphasizing the need for region-specific parameterization. OA was identified as the primary scattering contributor in both cities, though inorganic species became critical under high-pollution conditions. These findings suggest targeted strategies: reducing VOC emissions in southern China and controlling NOx in northern industrial areas to improve winter visibility and air quality. Full article
(This article belongs to the Section Aerosols)
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25 pages, 5441 KB  
Article
Assessment of Air Quality and Health Impact in Hanoi (Vietnam) Due to Traffic Emission—Seasonal Analysis and Traffic Emission Reduction Scenarios
by Quoc Bang Ho, Khue Vu, Hiep Duc Nguyen, Tam Nguyen, Hang Nguyen, Linh Do, Nguyen Huynh, Duyen Nguyen, Koji Fukuda and Makoto Kato
Atmosphere 2025, 16(11), 1301; https://doi.org/10.3390/atmos16111301 - 17 Nov 2025
Viewed by 491
Abstract
This study assesses air quality and health impact in Hanoi, Vietnam, using the Community Multiscale Air Quality (CMAQ) model and health impact assessment to evaluate the effectiveness of traffic emission reduction strategies under two scenarios. An updated emission inventory was used as the [...] Read more.
This study assesses air quality and health impact in Hanoi, Vietnam, using the Community Multiscale Air Quality (CMAQ) model and health impact assessment to evaluate the effectiveness of traffic emission reduction strategies under two scenarios. An updated emission inventory was used as the input data for the CMAQ model. The Weather Research and Forecasting (WRF-CMAQ) model (version 5.4), incorporating the CB6 chemical mechanism, was applied alongside a calibrated meteorological model to simulate pollutant dispersion. The model achieved strong performance in PM2.5 simulation, with a correlation coefficient (R) of 0.78, an index of agreement (IOA) of −0.5, a Normalized Mean Bias (NMB) of 7.11%, and a normalized mean error (NME) of 28.51%. Seasonal analysis revealed higher concentrations of CO, NO2, O3, and SO2 in January compared to July, driven by traffic and industrial emissions. Improved air quality in July was attributed to favorable meteorological conditions, such as increased rainfall and clean airflows from the sea. Spatial distribution highlighted elevated pollutant levels in urban areas, while PM2.5 was significantly influenced by long-range transport and atmospheric processes. However, fine dust concentrations remained high in suburban areas, driven by secondary emissions and nearby industrial zones. An emission reduction scenario based on the Hanoi city policy decree focusing on traffic sources demonstrated its potential to reduce NO2, SO2, and PM2.5 concentrations, though the impacts varied across time and space. Health impact due to population exposure to PM2.5 shows that the densely populated suburbs surrounding the urban core have the largest impact in terms of mortality and cardiovascular diseases hospitalization. As PM2.5 has the largest impact on these two health endpoints, only PM2.5 impact assessment is performed. Health impact due to air pollution is higher in January (dry season) with estimated 625 deaths and 124 cardiovascular diseases (cvd) hospitalization as compared with estimated 94 deaths and 18 cvd hospitalization in July (wet season). One of the research questions posed by the city authority is whether converting diesel buses to electric buses can yield environmental and health benefits. Our work shows that the scenario based on Hanoi city decree of replacing 50% of fossil fuel combustion buses with electric buses by 2035 does not yield perceptible change in mortality health effect. This is due to emission from buses being small as compared to those from the whole transport sector and other sectors. This study emphasizes the need for integrated, targeted emission control strategies to address spatial and temporal variability in pollution. The findings offer valuable insights for policymakers to develop effective measures in urban planning for improving air quality and protecting the health of people in Hanoi. Full article
(This article belongs to the Section Air Quality and Health)
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25 pages, 8985 KB  
Article
Estimation of PM2.5 Concentrations Using Fusion 3 km AOD of Two-Stage Models in Beijing–Tianjin–Hebei, China
by Yuchao Zhang, Xiaowen Xu, Zengfang Fu, Yan Wang, Yangyang Zhao and Fuahao Zhang
Atmosphere 2025, 16(11), 1300; https://doi.org/10.3390/atmos16111300 - 17 Nov 2025
Viewed by 318
Abstract
Accurate estimation of fine particulate matter (PM2.5) concentrations at high spatial resolutions is crucial for air quality monitoring and health risk assessment, particularly in heavily polluted regions like Beijing–Tianjin–Hebei, China. This study proposes a two-stage modeling framework integrating Xtreme Gradient Boosting [...] Read more.
Accurate estimation of fine particulate matter (PM2.5) concentrations at high spatial resolutions is crucial for air quality monitoring and health risk assessment, particularly in heavily polluted regions like Beijing–Tianjin–Hebei, China. This study proposes a two-stage modeling framework integrating Xtreme Gradient Boosting (XGBoost) with geographically and temporally weighted regression (GTWR) to predict daily PM2.5 concentrations at a 3 km resolution. The first-stage XGBoost model captures complex nonlinear relationships between PM2.5 and predictor variables, while the second-stage GTWR model explicitly accounts for residual spatiotemporal autocorrelation. High-resolution (3 km) MODIS Collection 6.1 AOD data are fused with MERRA-2 reanalysis to address data gaps and enhance spatial coverage. Comprehensive evaluation across the monthly and seasonal scales demonstrates that the XGBoost-GTWR hybrid model (R2 = 0.95, RMSE = 5.15 µg/m3, MAE = 3.66 µg/m3) significantly outperforms individual models (GWR, GTWR, XGBoost) and alternative hybrid models (XGBoost-GWR). The estimated PM2.5 concentrations exhibit distinct spatiotemporal patterns, with winter showing the highest pollution levels (100.3 µg/m3 as the average winter value in Handan), while spatial hotspots are consistently identified in central and southern Beijing–Tianjin–Hebei (BTH). Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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15 pages, 2391 KB  
Article
Research on the Impact of Typical SCR Faults on NOx Emission Deterioration of Heavy-Duty Vehicles
by Hao Zhang, Xiaofei Cao, Fengbin Wang, Hanzhengnan Yu, Jingyuan Li and Yu Liu
Atmosphere 2025, 16(11), 1299; https://doi.org/10.3390/atmos16111299 - 17 Nov 2025
Viewed by 307
Abstract
Faults of the selective catalytic reduction (SCR) significantly exacerbate nitrogen oxide (NOx) emissions from heavy-duty vehicles, thereby posing a severe hazard to atmospheric environmental quality. Currently, the paucity of systematic studies on NOx emission degradation induced by typical SCR faults has severely hindered [...] Read more.
Faults of the selective catalytic reduction (SCR) significantly exacerbate nitrogen oxide (NOx) emissions from heavy-duty vehicles, thereby posing a severe hazard to atmospheric environmental quality. Currently, the paucity of systematic studies on NOx emission degradation induced by typical SCR faults has severely hindered the advancement of precise emission regulation for heavy-duty vehicles in China. To address this critical gap, this study investigates the impact of typical SCR faults on NOx emission deterioration from heavy-duty vehicles. Initially, leveraging the China heavy-duty commercial vehicle test cycle as the benchmark, heavy-duty vehicle emission tests were designed and conducted under typical SCR faults. Emission datasets were acquired for three typical SCR faults—namely nozzle circuit disconnected fault, upstream temperature sensor inaccuracy fault, and urea-water replacement fault—as well as under normal operating conditions. Building upon these data, three representative scenarios were established by integrating vehicle operating condition, fuel consumption levels, and vehicle specific power states, enabling systematic quantification of the extent of NOx emission deterioration caused by each SCR fault. The findings reveal that the NOx emissions deterioration caused by urea-water replacement fault is the most severe, followed by nozzle circuit disconnected fault, and the impact of upstream temperature sensor inaccuracy fault is the least. This research provides crucial support for identifying key targets in emission control and enhancing the precision of heavy-duty vehicle emission regulation. Relevant authorities should prioritize cracking down on intentional non-compliant practices such as urea water substitution to safeguard a healthy and sustainable atmospheric environment. Full article
(This article belongs to the Special Issue Traffic Related Emission (3rd Edition))
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19 pages, 5228 KB  
Article
Predicting Lightning from Near-Surface Climate Data in the Northeastern United States: An Alternative to CAPE
by Charlotte Uden, Patrick J. Clemins and Brian Beckage
Atmosphere 2025, 16(11), 1298; https://doi.org/10.3390/atmos16111298 - 17 Nov 2025
Viewed by 348
Abstract
Lightning is a critical driver of natural wildfire ignition and ecosystem dynamics, but existing prediction models rely on upper-air predictors such as convective available potential energy (CAPE) that are absent from paleoclimate reconstructions. To enable long-term reconstructions of lightning activity, we developed and [...] Read more.
Lightning is a critical driver of natural wildfire ignition and ecosystem dynamics, but existing prediction models rely on upper-air predictors such as convective available potential energy (CAPE) that are absent from paleoclimate reconstructions. To enable long-term reconstructions of lightning activity, we developed and evaluated statistical models based solely on near-surface climate variables: temperature, precipitation, humidity, surface air pressure, wind, and shortwave radiation. Using ERA5 reanalysis and Vaisala Lightning Detection Network data (2005–2010) for the Northeastern United States, we compared linear regression, gamma generalized linear models, and Bayesian gamma models against CAPE-based benchmarks. While CAPE-based models outperformed models based on individual near-surface predictors, they showed limitations when predicting temporal anomalies. Models incorporating multiple near-surface predictors consistently outperformed CAPE-based models, reproducing observed spatial gradients, interannual variability, and strike rate distributions. Gamma generalized linear models achieved the strongest overall performance, balancing realistic, non-negative predictions with accuracy across error- and correlation-based metrics, while Bayesian models better captured the distribution of strike rates but sacrificed spatial precision. Our results demonstrate that near-surface predictors provide a viable alternative for lightning prediction when upper-air data are unavailable, providing a methodological pathway for reconstructing long-term seasonal lightning variability and its role in climate-fire interactions. Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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16 pages, 7883 KB  
Article
Topographic Algebraic Rossby Solitary Waves: A Study Using Physics-Informed Neural Networks
by Weiqi Zhang, Quansheng Liu, Liqing Yue and Ruigang Zhang
Atmosphere 2025, 16(11), 1297; https://doi.org/10.3390/atmos16111297 - 17 Nov 2025
Viewed by 241
Abstract
This study explores the influence of topography on Rossby solitary waves by analyzing the dynamics in both inner and outer regions, with solutions matched at the boundary. Using perturbation methods, the Benjamin–Davis–Ono (BDO) equation is derived from the shallow-water equations to characterize the [...] Read more.
This study explores the influence of topography on Rossby solitary waves by analyzing the dynamics in both inner and outer regions, with solutions matched at the boundary. Using perturbation methods, the Benjamin–Davis–Ono (BDO) equation is derived from the shallow-water equations to characterize the amplitude evolution of Rossby algebraic solitary waves. The Physics-Informed Neural Networks (PINNs) method is applied to numerically solve the BDO equation, effectively capturing its nonlinear behavior and simulating the propagation of Rossby algebraic solitary waves under diverse topographic and external conditions. The results demonstrate that variations in key parameters significantly alter topographic effects, thereby impacting the amplitude of solitary waves. These findings provide valuable insights into the potential consequences of global warming and other external disturbances on the behavior of Rossby waves. Full article
(This article belongs to the Section Climatology)
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25 pages, 8960 KB  
Article
Divergent Urban Ozone Responses to Straw Burning in Northern China from Observational Data: Roles of Meteorology and Photochemistry
by Wannan Wang and Chunjiao Wang
Atmosphere 2025, 16(11), 1296; https://doi.org/10.3390/atmos16111296 - 16 Nov 2025
Viewed by 307
Abstract
Open burning of crop residue is a major source of air pollutants in China. While a nationwide straw burning ban implemented in 2016 has proven effective in reducing primary emissions, its impact on ozone (O3), a key pollutant detrimental to human [...] Read more.
Open burning of crop residue is a major source of air pollutants in China. While a nationwide straw burning ban implemented in 2016 has proven effective in reducing primary emissions, its impact on ozone (O3), a key pollutant detrimental to human health, remain poorly quantified. This study aims to assess the impact of straw burning on downwind urban O3 pollution and to investigate the complex mechanisms governing O3 changes resulting from transported agricultural fire plumes. Here, using multi-satellite data and ground observations from 2013 to 2020, this study developed a method to identify smoke-affected days and estimate plume transport time over northern China. The results show that the straw burning ban effectively reduced peak concentrations of particulate matter (PM2.5) during harvest seasons. However, O3 responses on smoke-affected days were heterogeneous, showing both increases and decreases. The random forest model revealed the meteorological and chemical drivers of O3 variability. Elevated formaldehyde (HCHO) and temperatures promote O3 production, while higher NO2 and relative humidity enhance its titration. Trajectory analysis further decoupled the mechanisms that O3 and HCHO enhancements were primarily driven by local photochemistry, whereas NO2 increases were attributable to regional transport and mixing with anthropogenic pollution. This study underscores the necessity for integrated air quality management strategies that account for the complex trade-offs between PM2.5 and O3 to fully realize the public health benefits of emission control policies. Full article
(This article belongs to the Section Air Quality)
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21 pages, 4252 KB  
Article
Improving the Prediction of Land Surface Temperature Using Hyperparameter-Tuned Machine Learning Algorithms
by Anurag Mishra, Anurag Ohri, Prabhat Kumar Singh, Nikhilesh Singh and Rajnish Kaur Calay
Atmosphere 2025, 16(11), 1295; https://doi.org/10.3390/atmos16111295 - 15 Nov 2025
Viewed by 356
Abstract
Land surface temperature (LST) is a critical variable for understanding energy exchanges and water balance at the Earth’s surface, as well as for calculating turbulent heat flux and long-wave radiation at the surface–atmosphere interface. Remote sensing techniques, particularly using satellite platforms like Landsat [...] Read more.
Land surface temperature (LST) is a critical variable for understanding energy exchanges and water balance at the Earth’s surface, as well as for calculating turbulent heat flux and long-wave radiation at the surface–atmosphere interface. Remote sensing techniques, particularly using satellite platforms like Landsat 8 OLI/TIRS and Sentinel-2A, have facilitated detailed LST mapping. Sentinel-2 offers high spatial and temporal resolution multispectral data, but it lacks thermal infrared bands, which Landsat 8 can provide a 30 m resolution with less frequent revisits compared to Sentinel-2. This study employs Sentinel-2 spectral indices as independent variables and Landsat 8-derived LST data as the target variable within a machine-learning framework, enabling LST prediction at a 10 m resolution. This method applies grid search-based hyperparameter-tuned machine learning algorithms—Random Forest (RF), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), and k-Nearest Neighbours (kNN)—to model complex nonlinear relationships between the spectral indices (NDVI, NDWI, NDBI, and BSI) and LST. Grid search, combined with cross-validation, enhanced the model’s prediction accuracy for both pre- and post-monsoon seasons. This approach surpasses earlier methods that either employed untuned models or failed to integrate Sentinel-2 data. This study demonstrates that capturing urban thermal dynamics at fine spatial and temporal scales, combined with tuned machine learning models, can enhance the capability of urban heat island monitoring, climate adaptation planning, and sustainable environmental management models. Full article
(This article belongs to the Special Issue UHI Analysis and Evaluation with Remote Sensing Data (2nd Edition))
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16 pages, 1358 KB  
Article
Quantification of Heavy Metals in Indoor Dust for Health Risk Assessment in Macao
by Thomas M. T. Lei, Wenlong Ye, Yuyang Liu, Wan Hee Cheng, Altaf Hossain Molla, L.-W. Antony Chen and Shuiping Wu
Atmosphere 2025, 16(11), 1294; https://doi.org/10.3390/atmos16111294 - 15 Nov 2025
Viewed by 465
Abstract
The presence of heavy metals plays a significant role in indoor air quality, which poses a serious public health problem since most of the population spends over 90% of their time in indoor environments. This work investigates heavy metals in indoor dust across [...] Read more.
The presence of heavy metals plays a significant role in indoor air quality, which poses a serious public health problem since most of the population spends over 90% of their time in indoor environments. This work investigates heavy metals in indoor dust across different occupational settings in Macao. Field sampling was conducted in five representative locations, which included restaurants, student dormitories, auto repair shops, offices, and parking security rooms, with a total of 11 samples collected in this study. Dust in the form of particulate matter was collected from air conditioning filters to quantify 14 heavy metal contents. The PMF model was applied for source apportionments of the heavy metals, while a health exposure model was used to assess health risks and evaluate the non-carcinogenic and carcinogenic risks in the five representative workplaces. The PMF model identified six major pollution sources: traffic emissions (23.800%), building materials (21.600%), cooking activities (18.500%), chemicals (15.200%), electronic devices (12.300%), and outdoor seaport activities (8.600%). The health risk assessment showed that the overall non-carcinogenic risk (HI = 6.160 × 10−6 for inhalation, 1.720 × 10−3 for oral ingestion, and 2.270 × 10−5 for dermal contact) and total HI (1.749 × 10−3) and carcinogenic risk (6.570 × 10−9) were below the safety threshold, showing minimal health risk problems. Nevertheless, nickel and chromium were identified as the main contributors to potential long-term risks. 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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12 pages, 1550 KB  
Article
Deflection of Electric Streamer Channels in an Applied Electric Field
by Vernon Cooray, Gerald Cooray, Hasupama Jayasinghe, Farhad Rachidi and Marcos Rubinstein
Atmosphere 2025, 16(11), 1293; https://doi.org/10.3390/atmos16111293 - 14 Nov 2025
Viewed by 270
Abstract
Understanding how the path of streamers is influenced by background electric fields is crucial in leader progression models and electrical breakdown models in long gaps. While numerous advanced models of streamers exist, applying them to leader progression models to track streamer movement remains [...] Read more.
Understanding how the path of streamers is influenced by background electric fields is crucial in leader progression models and electrical breakdown models in long gaps. While numerous advanced models of streamers exist, applying them to leader progression models to track streamer movement remains computationally intensive and impractical. In this study, we employ one of the simplest streamer models available in the literature to investigate how streamers are deflected in the presence of background electric fields. Our analysis identifies the key parameters that govern this interaction. Additionally, we estimate the time and length scales over which streamers are diverted by a background electric field of a specified strength. Full article
(This article belongs to the Special Issue Feature Papers in Atmospheric Techniques, Instruments, and Modeling (2nd Edition))
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31 pages, 6661 KB  
Article
Hybrid Deep Learning Models for Predicting Meteorological Variables Associated with Santa Ana Wind Conditions in the Guadalupe Basin
by Yeraldin Serpa-Usta, Dora-Luz Flores, Alvaro López-Ramos, Carlos Fuentes, Franklin Muñoz-Muñoz, Neila María González Tejada and Alvaro Alberto López-Lambraño
Atmosphere 2025, 16(11), 1292; https://doi.org/10.3390/atmos16111292 - 14 Nov 2025
Viewed by 475
Abstract
Santa Ana winds are extreme meteorological events that strongly affect the U.S.–Mexico border region, often associated with droughts, high fire risk, and hydrological imbalance. Understanding the temporal behavior of key atmospheric variables during these events is crucial for integrated water resource management in [...] Read more.
Santa Ana winds are extreme meteorological events that strongly affect the U.S.–Mexico border region, often associated with droughts, high fire risk, and hydrological imbalance. Understanding the temporal behavior of key atmospheric variables during these events is crucial for integrated water resource management in semi-arid regions such as the Guadalupe Basin in northern Baja California. In this study, we explored the predictive capability of several hybrid deep learning architectures—Long Short-Term Memory (LSTM), Convolutional Neural Network combined with LSTM (CNN–LSTM), and Bidirectional LSTM with Attention (BiLSTM–Attention)—to model the temporal evolution of wind speed, wind direction, temperature, relative humidity, and atmospheric pressure using Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis data from 1980 to 2020. Model performance was evaluated using RMSE, MAE, and R2 metrics and compared against persistence and climatology baselines. The BiLSTM–Attention model achieved the best overall performance, showing particularly high accuracy for temperature (R2 = 0.95) and relative humidity (R2 = 0.76), while maintaining angular errors below 35° for wind direction. The results demonstrate the potential of hybrid deep learning models to capture nonlinear temporal dependencies in meteorological time series and provide a methodological framework to enhance hydrometeorological understanding and water resource management in the Guadalupe Basin under Santa Ana wind conditions. Full article
(This article belongs to the Section Meteorology)
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16 pages, 3703 KB  
Article
Furnace Air Filter Replacement Practices and Implications for Indoor Air Quality: A Pilot Study
by Daniel L. Mendoza, Lauren Piper Christian, Erik T. Crosman and Adrienne Cachelin
Atmosphere 2025, 16(11), 1291; https://doi.org/10.3390/atmos16111291 - 13 Nov 2025
Viewed by 339
Abstract
Utah typically experiences 18 days with high fine particulate matter (PM2.5) levels exceeding the National Ambient Air Quality Standards per year. In August of 2022, Salt Lake City Mayor Erin Mendenhall convened an Indoor Air Quality Summit, during which experts in [...] Read more.
Utah typically experiences 18 days with high fine particulate matter (PM2.5) levels exceeding the National Ambient Air Quality Standards per year. In August of 2022, Salt Lake City Mayor Erin Mendenhall convened an Indoor Air Quality Summit, during which experts in healthcare, industrial hygiene, and atmospheric science, among others, expressed the need to prioritize indoor air quality interventions more within the state. We conducted a furnace filter exchange pilot project that involved 11 families in Salt Lake City’s Westside. These families completed a survey regarding air quality-related concerns while researchers took air quality measurements—both inside and outside the residence. The goals of this pilot study were to gather data about the participants’ indoor and outdoor air quality perceptions, how frequently they changed their home air filters, and any barriers they experienced. In addition, this study developed a proof of concept demonstrating collecting preliminary indoor and outdoor air quality data and furnace filter deposition information alongside the survey. The survey results were limited by a small sample size (11 participants); however, among those sampled we found that residents are acutely concerned about outdoor air quality but are less worried about indoor air quality. We measured substantially lower indoor PM2.5 levels compared to ambient air and found a wide range of filter replacement times from those less than a month to over two years. Our research team learned not only about indoor air quality conditions and resident perceptions, but also about the needs of community members including access to filters, health education, and the need to allow more time to build trust between researchers and residents. Full article
(This article belongs to the Special Issue Sources Influencing Air Pollution and Their Control)
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32 pages, 8546 KB  
Article
Research on the Cumulative Dust Suppression Effect of Foam and Dust Extraction Fan at Continuous Miner Driving Face
by Jiangang Wang, Jiaqi Du, Kai Jin, Tianlong Yang, Wendong Zhou, Xiaolong Zhu, Hetang Wang and Kai Zhang
Atmosphere 2025, 16(11), 1290; https://doi.org/10.3390/atmos16111290 - 13 Nov 2025
Viewed by 387
Abstract
The heading face is one of the zones most severely affected by dust pollution in underground coal mines, and dust control becomes even more challenging during roadway excavation with continuous miners. To improve dust mitigation in environments characterized by intense dust generation, high [...] Read more.
The heading face is one of the zones most severely affected by dust pollution in underground coal mines, and dust control becomes even more challenging during roadway excavation with continuous miners. To improve dust mitigation in environments characterized by intense dust generation, high ventilation demand, and large cross-sectional areas, this study integrates numerical simulations, laboratory experiments, and field tests to investigate the physicochemical properties of dust, airflow distribution, dust migration behavior, and a comprehensive dust control strategy combining airflow regulation, foam suppression, and dust extraction fan systems. The results show that dust dispersion patterns differ markedly between the left-side advancement and right-side advancement of the roadway; however, the wind return side of the continuous miner consistently exhibits the highest dust concentrations. The most effective purification of dust-laden airflow is achieved when the dust extraction fan delivers an airflow rate of 500 m3/min and is positioned behind the continuous miner on the return side. After optimization of foam flow rate and coverage based on the cutting head structure of the continuous miner, the dust suppression efficiency reached 78%. With coordinated optimization and on-site implementation of wall-mounted ducted airflow control, foam suppression, and dust extraction fan systems, the total dust reduction rate at the heading face reached 95.2%. These measures substantially enhance dust control effectiveness, improving mine safety and protecting worker health. The resulting reduction in dust concentration also improves visibility for underground intelligent equipment and provides practical guidance for industrial application. Full article
(This article belongs to the Section Air Pollution Control)
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17 pages, 1111 KB  
Article
Mitigating Ammonia Emissions from Liquid Manure Using a Commercially Available Additive Under Real-Scale Farm Conditions
by Marcello Ermido Chiodini, Michele Costantini, Michele Zoli, Daniele Aspesi, Lorenzo Poggianella and Jacopo Bacenetti
Atmosphere 2025, 16(11), 1289; https://doi.org/10.3390/atmos16111289 - 12 Nov 2025
Viewed by 408
Abstract
Ammonia (NH3) is a major anthropogenic pollutant originating from agricultural activity, particularly livestock operations. NH3 emissions from livestock slurry storage pose risks to environmental quality and human health. Reducing NH3 emissions aligns with several United Nations Sustainable Development Goals [...] Read more.
Ammonia (NH3) is a major anthropogenic pollutant originating from agricultural activity, particularly livestock operations. NH3 emissions from livestock slurry storage pose risks to environmental quality and human health. Reducing NH3 emissions aligns with several United Nations Sustainable Development Goals (SDGs), including SDG 3, SDG 12, SDG 14, and SDG 15. This study evaluates the performance of the commercially available SOP® LAGOON additive under real-scale farm conditions for mitigating NH3 emissions. Two adjacent slurry storage tanks of a dairy farm in Northern Italy were monitored from 27 May to 7 September: one treated with SOP® LAGOON and one left untreated (serving as a control). In the first month, the treated tank showed a 77% reduction in NH3 emissions. Emissions from the treated tank remained consistently lower than those from the control throughout the monitoring period, reaching an 87% reduction relative to the baseline levels by the end of the period. The results suggest that SOP® LAGOON is an effective and scalable strategy for reducing NH3 emissions from liquid manure storage, with practical implications for farmers and policy makers in regard to designing sustainable manure management practices. Full article
(This article belongs to the Section Air Pollution Control)
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22 pages, 5851 KB  
Article
A Multi-Stage Deep Learning Framework for Multi-Source Cloud Top Height Retrieval from FY-4A/AGRI Data
by Yinhe Cheng, Long Shen, Jiulei Zhang, Hongjian He, Xiaomin Gu, Shengxiang Wang and Tinghuai Ma
Atmosphere 2025, 16(11), 1288; https://doi.org/10.3390/atmos16111288 - 12 Nov 2025
Viewed by 379
Abstract
Cloud Top Height (CTH), defined as the altitude of the highest cloud layer above mean sea level, is a crucial geophysical parameter for quantifying cloud radiative effects, analyzing severe weather systems, and improving climate models. To enhance the accuracy of CTH retrieval from [...] Read more.
Cloud Top Height (CTH), defined as the altitude of the highest cloud layer above mean sea level, is a crucial geophysical parameter for quantifying cloud radiative effects, analyzing severe weather systems, and improving climate models. To enhance the accuracy of CTH retrieval from Fengyun-4A (FY-4A) satellite data, this study proposes a multi-stage deep learning framework that progressively refines cloud parameter estimation. The method utilizes cloud information from the FY-4A/AGRI (Advanced Geosynchronous Radiation Imager) Level 1 calibrated scanning imager radiance data product to construct a multi-source data fusion neural network model. The model inputs combine multi-channel radiance data with cloud parameters, including Cloud Top Temperature (CTT) and Cloud Top Pressure (CTP). We used the CTH measurement data from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite as a reference to verify the model output. Results demonstrate that the proposed multi-stage model significantly improves retrieval accuracy. Compared to the official FY-4A CTH product, the Mean Absolute Error (MAE) was reduced by 49.12% to 2.03 km, and the Pearson Correlation Coefficient (PCC) reached 0.85. To test the applicability of the model under complex weather conditions, we applied it to the CTH inversion of the double typhoon event on 10 August 2019. The model successfully characterized the spatial distribution of CTH within the typhoon regions. The results are consistent with the National Satellite Meteorological Centre (NSMC) reports and clearly reveal the different intensity evolutions of the two typhoons. This research provides an effective solution for high-precision retrieval of high-level cloud CTH at a large scale, using geostationary meteorological satellite remote sensing data. Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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20 pages, 11111 KB  
Article
Long-Term Trends and Seasonally Resolved Drivers of Surface Albedo Across China Using GTWR
by Jiqiang Niu, Ziming Wang, Hao Lin, Hongrui Li, Zijian Liu, Mengyang Li, Xiaodong Deng, Bohan Wang, Tong Wu and Junkuan Zhu
Atmosphere 2025, 16(11), 1287; https://doi.org/10.3390/atmos16111287 - 12 Nov 2025
Viewed by 354
Abstract
Amid accelerating global warming, surface albedo is a key indicator and regulator of how Earth’s surface reflects solar radiation, directly affecting the planetary radiation balance and climate. In this paper, we combined MODIS shortwave albedo (MCD43A3, 500 m), MODIS NDVI (MOD13A3, 1 km; [...] Read more.
Amid accelerating global warming, surface albedo is a key indicator and regulator of how Earth’s surface reflects solar radiation, directly affecting the planetary radiation balance and climate. In this paper, we combined MODIS shortwave albedo (MCD43A3, 500 m), MODIS NDVI (MOD13A3, 1 km; NDVI = normalized difference vegetation index) and 1-km gridded meteorological data to analyze the spatiotemporal variations of surface albedo across China during 2001–2020 at a gridded scale. Temporal trends were quantified with the Theil–Sen slope and the Mann–Kendall test, and the seasonal contributions of NDVI, air temperature, and precipitation were assessed with a geographically and temporally weighted regression (GTWR) model. China’s mean annual shortwave albedo was 0.186 and showed a significant decline. Attribution indicates NDVI is the dominant driver (~48% of total change), followed by temperature (~27%) and precipitation (~25%). Seasonally, NDVI explains ~43.94–52.02% of the variation, ~26.81–28.07% of the temperature, and ~21.17–28.57% of the precipitation. Clear spatial patterns emerge. In high-latitude and high-elevation snow-dominated regions, albedo tends to decrease with warmer conditions and increase with greater precipitation. In much of eastern China, albedo is generally positively associated with temperature and negatively with precipitation. NDVI—reflecting vegetation greenness and canopy structure—captures the effects of vegetation greening, canopy densification, and land-cover change that reduce surface reflectivity by enhancing shortwave absorption. Temperature and precipitation affect albedo primarily by regulating vegetation growth. This study goes beyond correlation mapping by combining robust trend detection (Theil–Sen + MK) with GTWR to resolve seasonally varying, non-stationary controls on albedo at 1-km over 20 years. By explicitly separating snow-covered and snow-free conditions, we quantify how NDVI, temperature, and precipitation contributions shift across climate zones and seasons, providing a reproducible, national-scale attribution that can inform ecosystem restoration and land-surface radiative management. Full article
(This article belongs to the Special Issue Effects of Natural and Anthropogenic Factors on Climate and Environment (3rd Edition))
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20 pages, 4623 KB  
Article
Enhancing Forecasting Capabilities Through Data Assimilation: Investigating the Core Role of WRF 4D-Var in Multidimensional Meteorological Fields
by Yujiayi Deng, Xiaotong Wang, Xinyi Fu, Nian Wang, Hongyuan Yang, Shuhui Zhao, Xiurui Guo, Jianlei Lang, Ying Zhou and Dongsheng Chen
Atmosphere 2025, 16(11), 1286; https://doi.org/10.3390/atmos16111286 - 12 Nov 2025
Viewed by 391
Abstract
As climate change intensifies, enhancing numerical weather prediction (NWP) accuracy has been increasingly critical. While data assimilation optimizes NWP initial conditions, its effectiveness over complex terrain requires further systematic evaluation. This study implemented a high-resolution WRF/4D-Var data assimilation framework, overcoming its inherent limitation [...] Read more.
As climate change intensifies, enhancing numerical weather prediction (NWP) accuracy has been increasingly critical. While data assimilation optimizes NWP initial conditions, its effectiveness over complex terrain requires further systematic evaluation. This study implemented a high-resolution WRF/4D-Var data assimilation framework, overcoming its inherent limitation of not supporting two-layer nested assimilation across domains by designing a two-layer nested “assimilation-forecast” workflow. Representative winter and summer cases from February and June 2019 were selected to evaluate improvements in near-surface and upper-air meteorological parameters. The results indicated that the 4D-Var data assimilation significantly improved the correlation coefficients of near-surface variables during winter by 2.9% (temperature), 14.5% (relative humidity), 6.6% (wind speed), and 10.4% (wind direction), with even greater improvements observed in summer reaching 13.3%, 5.8%, 35.3%, and 42.3%, respectively. Meanwhile, 4D-Var considerably enhanced the atmospheric vertical profiling, with the middle troposphere (300–700 hPa) exhibiting the most pronounced improvement. Among different surface types, water bodies exhibited the strongest assimilation response. Results also revealed systematic corrections to the background fields, with February exhibiting more uniform adjustments in contrast to June’s complex spatiotemporal patterns. Positive effects persisted throughout the 24-h forecasts, with the maximum benefit occurring within the first 12 h. These results demonstrate the effectiveness of 4D-Var in regional meteorological forecasting, highlighting its value for constructing high-precision multidimensional meteorological fields to support both weather and air quality simulations. Full article
(This article belongs to the Section Meteorology)
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18 pages, 5250 KB  
Article
Assessment of Accuracy of COSMIC and KOMPSAT GNSS Radio Occultation Temperature and Pressure Measurements over the Philippines
by Karl Philippe A. Descalzo and Ernest P. Macalalad
Atmosphere 2025, 16(11), 1285; https://doi.org/10.3390/atmos16111285 - 11 Nov 2025
Viewed by 676
Abstract
Radio occultation (RO) is a technique used for measuring planetary atmosphere properties by orbiting satellites, like temperature, pressure, and water vapor. Typically using Global Navigation Satellite System (GNSS) signals, this technique is often assessed with atmospheric properties measured by radiosonde (RS) stations around [...] Read more.
Radio occultation (RO) is a technique used for measuring planetary atmosphere properties by orbiting satellites, like temperature, pressure, and water vapor. Typically using Global Navigation Satellite System (GNSS) signals, this technique is often assessed with atmospheric properties measured by radiosonde (RS) stations around the world. The aim of this study is to assess the radio occultation temperature and pressure profiles from the Constellation Observing System for Meteorology, Ionosphere and Climate 2 (COSMIC-2) and Korean Multi-purpose Satellite 5 (KOMPSAT-5) satellites using data from collocated radiosonde stations over the Philippines. Their deviations are analyzed using their mean and standard deviations. COSMIC-2 and KOMPSAT-5 temperature and pressure from the atmPrf product are in good agreement with radiosondes above 5–10 km, where moisture is negligible. COSMIC-2 has good agreement with radiosonde stations in 2020. KOMPSAT-5 has good agreement with radiosonde stations in 2019–2020. For both satellites, the deviations are larger within the lower troposphere, compared to heights above ~5–10 km. For both years, KOMPSAT-5 deviations are higher during the summer season until 10 km. For COSMIC-2, deviations are higher during the summer and autumn seasons. The quality of these results shows COSMIC and KOMPSAT as possible high-quality applications for weather prediction. In addition to providing comparable high-precision data, radio occultation can provide more dense coverage of areas without radiosondes. Full article
(This article belongs to the Special Issue Feature Papers in Atmospheric Techniques, Instruments, and Modeling (2nd Edition))
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17 pages, 2363 KB  
Article
Analysis of Consecutive Dry Days in the MATOPIBA Region During the Rainy and Dry Seasons
by Daniele Tôrres Rodrigues, Flavia Ferreira Batista, Lara de Melo Barbosa Andrade, Helder José Farias da Silva, Jório Bezerra Cabral Júnior, Marcos Samuel Matias Ribeiro, Jean Souza dos Reis, Josiel dos Santos Silva, Fabrício Daniel dos Santos Silva and Claudio Moisés Santos e Silva
Atmosphere 2025, 16(11), 1284; https://doi.org/10.3390/atmos16111284 - 11 Nov 2025
Viewed by 532
Abstract
Climate change and its impacts on precipitation patterns have intensified the occurrence of prolonged dry periods in agricultural regions of Brazil, particularly in the MATOPIBA region (comprising the states of Maranhão, Tocantins, Piauí, and Bahia). This study analyzes the seasonal variability and trends [...] Read more.
Climate change and its impacts on precipitation patterns have intensified the occurrence of prolonged dry periods in agricultural regions of Brazil, particularly in the MATOPIBA region (comprising the states of Maranhão, Tocantins, Piauí, and Bahia). This study analyzes the seasonal variability and trends of the Consecutive Dry Days (CDDs) index in the MATOPIBA region from 1981 to 2023. Daily precipitation data from the Brazilian Daily Weather Gridded Data (BR-DWGD) dataset were used for the analysis. The novelty of this work lies in its focus on the seasonal characterization of CDD across the entire MATOPIBA field of agriculture, addressing the following main research question: how have the frequency and persistence of dry spells evolved during the rainy and dry seasons over the past four decades? The methodology involved trend detection using the Mann–Kendall test and Sen’s Slope estimator. The results indicated that during the rainy season, the average CDD ranged from 20 to 60 days, with higher values concentrated in the states of Piauí and Bahia. In contrast, during the dry period, averages exceeded 100 days across most of the region. Trend analysis revealed a significant increase in CDD over extensive areas, particularly in Tocantins and Southern Bahia. The increasing trends were estimated at 1 to 4 days per decade during the rainy season and 4 to 14 days per decade in the dry period. Although a decreasing CDD trend was observed in small areas of Northern Maranhão, possibly associated with the influence of the Intertropical Convergence Zone, the overall scenario indicates a greater persistence of long dry spells. This pattern suggests an increase in vulnerability to water scarcity and agricultural losses. These findings highlight the need for implementing adaptation strategies, such as the use of drought-tolerant cultivars, conservation management practices, irrigation expansion, and public policies aimed at promoting climate resilience in the MATOPIBA region. Full article
(This article belongs to the Section Climatology)
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Article
Urban Heat Hotspots in Tarragona: LCZ-Based Remote Sensing Assessment During Heatwaves
by Caterina Cimolai and Enric Aguilar
Atmosphere 2025, 16(11), 1283; https://doi.org/10.3390/atmos16111283 - 11 Nov 2025
Viewed by 458
Abstract
Heatwaves are intensifying across Mediterranean cities, where the Urban Heat Island (UHI) effect amplifies thermal stress. This study updates the spatial characterization of the Surface Urban Heat Island (SUHI) in Tarragona using multi-sensor remote sensing data within a Local Climate Zone (LCZ) framework. [...] Read more.
Heatwaves are intensifying across Mediterranean cities, where the Urban Heat Island (UHI) effect amplifies thermal stress. This study updates the spatial characterization of the Surface Urban Heat Island (SUHI) in Tarragona using multi-sensor remote sensing data within a Local Climate Zone (LCZ) framework. Land surface temperature, albedo, and the Normalized Difference Vegetation Index (NDVI) were analyzed during heatwaves from 2015–2025 to assess spatial patterns and drivers of urban heating. Results reveal a daytime urban cool island associated with low albedo and scarce vegetation, and a nocturnal SUHI caused by heat retention in dense built-up areas. High-resolution mapping identifies industrial and commercial zones as hotspots, while vegetated and water-covered areas act as cooling sites. These findings clarify the spatial dynamics and key biophysical controls of SUHI and provide an actionable basis for prioritizing locally tailored adaptation strategies in Mediterranean coastal cities. Full article
(This article belongs to the Special Issue Climate Extremes in Europe: Causes, Impact, and Solutions)
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