Atmosphere

35 pages, 3917 KB

Open AccessArticle

Mechanisms of Topographic Steering and Track Morphology of Typhoon-like Vortices over Complex Terrain: A Dynamic Model Approach

by Hung-Cheng Chen

Atmosphere 2026, 17(1), 60; https://doi.org/10.3390/atmos17010060 - 31 Dec 2025

Abstract

This study investigates the mechanisms of topographic steering and the resultant track morphology of typhoon-like vortices over complex terrain. Leveraging a dynamic model based on potential vorticity (PV) conservation, we conducted a comprehensive sensitivity analysis over both an idealized bell-shaped mountain and the [...] Read more.

This study investigates the mechanisms of topographic steering and the resultant track morphology of typhoon-like vortices over complex terrain. Leveraging a dynamic model based on potential vorticity (PV) conservation, we conducted a comprehensive sensitivity analysis over both an idealized bell-shaped mountain and the realistic topography of Taiwan. Results indicate that a triad of controls governs track evolution: vortex intensity (α), terrain geometry \({dh}_b^*/dt^* \), and interaction time (impinging angle γ). To quantify predictability, we introduce the Track Divergence Percentage (t_d), which partitions the phase space into distinct Track Diverging (TDZ) and Converging (TCZ) Zones. The results demonstrate that vortex intensity, terrain-induced forcing, and interaction time jointly organize a regime-dependent predictability landscape, characterized by distinct zones of track divergence and convergence separated by a dynamically balanced trajectory. This framework provides a physically interpretable explanation for why small perturbations in initial conditions can lead to qualitatively different track outcomes near complex terrain. Rather than aiming at direct forecast skill improvement, this study provides a physically interpretable diagnostic framework for understanding terrain-induced track sensitivity and uncertainty, with implications for interpreting ensemble spread in forecasting systems. Full article

(This article belongs to the Special Issue Typhoon/Hurricane Dynamics and Prediction (3rd Edition))

18 pages, 1316 KB

Open AccessArticle

Brake Particle PN and PM Emissions of Battery Electric Vehicles (BEVs): On-Vehicle Chassis Dynamometer Measurements

by Panayotis Dimopoulos Eggenschwiler, Daniel Schreiber and Nora Schüller

Atmosphere 2026, 17(1), 59; https://doi.org/10.3390/atmos17010059 - 31 Dec 2025

Abstract

Currently, brake particle emissions from traffic are considered one of the dominant sources of particulate matter in the atmosphere. A recent question concerns the contribution to brake particles of Battery Electric Vehicles (BEVs). The present work assesses brake particle emissions by measurements of [...] Read more.

Currently, brake particle emissions from traffic are considered one of the dominant sources of particulate matter in the atmosphere. A recent question concerns the contribution to brake particles of Battery Electric Vehicles (BEVs). The present work assesses brake particle emissions by measurements of particle number (PN) and mass (PM) of three light-duty BEVs. One front disc brake of each vehicle has been enclosed in a customized casing with appropriate ventilation for forming the aerosol. All three BEVs have been measured on a two-axis chassis dynamometer. The BEV relying more on electric braking (some 68% of the braking energy was covered by electric braking) had the lowest brake PN emissions over the (emissions) WLTC at 6.4 × 10⁹ km⁻¹ per front brake. This was less than half with respect to the other BEV (where only 52% of the braking energy was electric). PM emissions of the two vehicles were similar at 0.93 mg/km for PM < 12 μm and 0.65 mg/km for PM < 2.5 μm, both for one front brake. However, one of the measured BEVs had extraordinarily high PN emissions, some 23 times higher than the lowest-emitting BEV. The difference in PM was not as high, but was some four times higher. Full article

(This article belongs to the Special Issue Airborne Particles Emission and Generation Mechanisms of Brakes and Engine)

17 pages, 473 KB

Open AccessArticle

Household and Environmental Determinants of Adult Asthma Morbidity in Texas, 2019–2022

by Alexander Obeng, Taehyun Roh, Alejandro Moreno-Rangel and Genny Carrillo

Atmosphere 2026, 17(1), 58; https://doi.org/10.3390/atmos17010058 - 31 Dec 2025

Abstract

Asthma continues to affect millions of adults in the United States, with indoor environmental exposures playing a major role in symptom burden and control. Limited research has examined the combined influence of multiple household and environmental determinants on adult asthma morbidity, particularly in [...] Read more.

Asthma continues to affect millions of adults in the United States, with indoor environmental exposures playing a major role in symptom burden and control. Limited research has examined the combined influence of multiple household and environmental determinants on adult asthma morbidity, particularly in diverse states such as Texas. We analyzed pooled data from 1596 Texas adults with asthma who completed the Asthma Call-Back Survey between 2019 and 2022. Multivariable logistic regression models, adjusted for survey design and demographic covariates, were used to examine associations between household and environmental determinants and four morbidity outcomes: asthma attacks, recent symptoms, sleep difficulty, and limited activity due to asthma. Current smoking, lack of bathroom or kitchen ventilation, and absence of air purifier use were consistently associated with higher odds of morbidity. Protective associations were observed for homes without mold, rodents, or furry pets. Disparities were also evident, with older adults, women, and non-Hispanic Black respondents reporting greater morbidity. These findings highlight the importance of addressing modifiable exposures such as indoor smoking, ventilation, and allergen control within comprehensive asthma management strategies. Targeted interventions that combine environmental modifications with health education may help reduce asthma disparities and improve the quality of life for adults with asthma. Full article

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

37 pages, 1187 KB

Open AccessReview

Vehicle Brake Wear Particles: Formation Mechanisms, Behavior, and Health Impacts with an Emphasis on Ultrafine Particles

by Jozef Salva, Miroslav Dado, Janka Szabová, Michal Sečkár, Marián Schwarz, Juraj Poništ, Miroslav Vanek, Anna Ďuricová and Martina Mordáčová

Atmosphere 2026, 17(1), 57; https://doi.org/10.3390/atmos17010057 - 31 Dec 2025

Abstract

Brake wear particles (BWPs) represent a major source of non-exhaust particulate matter from road traffic, contributing substantially to human exposure, particularly in urban environments. While traditionally associated with coarse and fine fractions, mounting evidence shows that brake systems emit large quantities of ultrafine [...] Read more.

Brake wear particles (BWPs) represent a major source of non-exhaust particulate matter from road traffic, contributing substantially to human exposure, particularly in urban environments. While traditionally associated with coarse and fine fractions, mounting evidence shows that brake systems emit large quantities of ultrafine particles (UFPs; <100 nm), which dominate number concentrations despite contributing little to mass. This paper synthesizes current knowledge on BWP formation mechanisms, physicochemical characteristics, environmental behavior, and toxicological effects, with a specific emphasis on UFPs. Mechanical friction and high-temperature degradation of pad and disc materials generate nanoscale primary particles that rapidly agglomerate yet retain ultrafine structural features. Reported real-world and laboratory number concentrations commonly range from 10³ to over 10⁶ particles/cm³, with diameters between 10 and 100 nm, rising sharply during intensive braking. Toxicological studies consistently demonstrate that UFP-rich and metal-laden BWPs, particularly those containing Fe, Cu, Mn, Cd, and Sb compounds, induce oxidative stress, inflammation, mitochondrial dysfunction, genotoxicity, and epithelial barrier disruption in human lung and immune cells. Ecotoxicological studies further reveal adverse impacts across aquatic organisms, plants, soil invertebrates, and mammals, with evidence of environmental persistence and food-chain transfer. Despite these findings, current regulatory frameworks address only the mass of particulate matter from brakes and omit UFP number-based limits, leaving a major gap in emission control. Full article

(This article belongs to the Special Issue From Traditional to Emerging Air Pollutants: Tools and Health Risk Assessment)

17 pages, 2883 KB

Open AccessArticle

Operational Short-Term Forecast of Marine Heatwaves in China’s Coastal Seas and Adjacent Offshore Waters

by Zhijie Li, Liying Wan, Zhaoyi Wang, Yang Liu and Jingjing Zheng

Atmosphere 2026, 17(1), 56; https://doi.org/10.3390/atmos17010056 - 31 Dec 2025

Abstract

In recent years, global sea surface temperature (SST) has risen steadily, with 2023 and 2024 breaking successive historical observation records, thus rendering marine heatwaves (MHWs) an unignorable new marine disaster. To scientifically mitigate and assess the impacts of MHW disasters on China’s coastal [...] Read more.

In recent years, global sea surface temperature (SST) has risen steadily, with 2023 and 2024 breaking successive historical observation records, thus rendering marine heatwaves (MHWs) an unignorable new marine disaster. To scientifically mitigate and assess the impacts of MHW disasters on China’s coastal waters, this study developed a monitoring and weekly forecast product for MHWs based on the OSTIA (Operational SST and Ice Analysis) SST observational fusion data and SST numerical forecast data. Evaluation shows the following: the quarterly average of the RMSE for the weekly MHWs intensity forecasts is 0.52 °C; and the quarterly average score for the weekly MHW’s category forecasts is 94.4. Characteristic analysis of 2024 MHWs reveals 93.7% of China’s coastal waters and adjacent areas experienced MHWs throughout the year, and the average monthly impact rate of MHWs is 43.8%. High-value areas of total days and cumulative intensity are concentrated in the central-eastern part of the Yellow Sea, which makes it the most severely affected area by MHW disasters in 2024. The weekly MHW’s forecast product developed in this study provides deterministic weekly forecasts of MHWs intensity and categories for China’s coastal waters. This product can serve as a guidance basis for MHW disaster prevention and mitigation, and help reduce losses caused by MHWs to the marine environment and marine economy. Full article

(This article belongs to the Special Issue Ocean Temperatures and Heat Waves)

20 pages, 4737 KB

Open AccessArticle

Magnetic Biomonitoring of PM in a Semi-Arid Urban Park of North-Central Mexico Using Tillandsia recurvata as a Particulate Matter Biocollector

by Ana G. Castañeda-Miranda, Harald N. Böhnel, Marcos A. E. Chaparro, Laura A. Pinedo-Torres, A. Rodríguez-Trejo, Rodrigo Castañeda-Miranda, Remberto Sandoval-Aréchiga, Víktor I. Rodríguez-Abdalá, Jose. R. Gomez-Rodriguez, Saúl Dávila-Cisneros and Salvador Ibarra Delgado

Atmosphere 2026, 17(1), 55; https://doi.org/10.3390/atmos17010055 - 31 Dec 2025

Abstract

This study assessed the spatial distribution and composition of airborne particulate matter within a 10 km long urban green corridor in Zacatecas, Mexico, using magnetic biomonitoring with Tillandsia recurvata and SEM-EDS particle characterization. A total of 44 samples were collected from distinct urban [...] Read more.

This study assessed the spatial distribution and composition of airborne particulate matter within a 10 km long urban green corridor in Zacatecas, Mexico, using magnetic biomonitoring with Tillandsia recurvata and SEM-EDS particle characterization. A total of 44 samples were collected from distinct urban park contexts (e.g., commercial zones, malls, bus stops), revealing mass-specific magnetic susceptibility χ values ranging from −6.71 to 61.1 × 10⁻⁸ m³ kg⁻¹. Three compositional groups were identified based on a PCA performed using elemental concentrations from SEM-EDS and magnetic data, which are associated with traffic emissions and industrial inputs. SEM-EDS images confirmed abundant magnetite-like particles (1–8 μm) with hazardous metals including Pb (up to 5.6 wt.%), Ba (up to 67.6 wt.%), and Cr (up to 31.5 wt.%). Wind direction data indicated predominant SSW–NNE transport, correlating with hotspots in central and northeastern park areas. Overall, vegetated zones exhibited markedly lower magnetic loads (mean χ = 8.84 × 10⁻⁸ m³ kg⁻¹) than traffic-exposed sites (mean χ = 17.27 × 10⁻⁸ m³ kg⁻¹), representing an approximate 50% reduction in magnetic particle accumulation, which highlights the effective role of continuous vegetation cover as a functional green barrier that attenuates the lateral transport and deposition of airborne particulate matter within the park. This research highlights the applicability of combined magnetic and microscopic techniques for evaluating the dynamics of airborne pollution in urban parks and supports their use for identifying both pollution hotspots and mitigation zones, reinforcing the role of urban green spaces as biofunctional filters in cities facing vehicular air pollution. Full article

(This article belongs to the Special Issue Indoor Air Pollution Monitoring: Multi-Pollutant Exposure and Risk Assessment)

16 pages, 5500 KB

Open AccessArticle

DWTPred-Net: A Spatiotemporal Ionospheric TEC Prediction Model Using Denoising Wavelet Transform Convolution

by Jie Li, Xiaofeng Du, Shixiang Liu, Yali Wang, Shaomin Li, Jian Xiao and Haijun Liu

Atmosphere 2026, 17(1), 54; https://doi.org/10.3390/atmos17010054 - 31 Dec 2025

Abstract

PredRNN is a spatiotemporal prediction model based on ST-LSTM units, capable of simultaneously extracting spatiotemporal features from ionospheric Total Electron Content (TEC). However, its internal convolutional operations require large kernels to capture low-frequency features, which can easily lead to model over-parameterization and consequently [...] Read more.

PredRNN is a spatiotemporal prediction model based on ST-LSTM units, capable of simultaneously extracting spatiotemporal features from ionospheric Total Electron Content (TEC). However, its internal convolutional operations require large kernels to capture low-frequency features, which can easily lead to model over-parameterization and consequently limit its performance. Although some studies have employed wavelet transform convolution (WTConv) to improve feature extraction efficiency, the introduced noise interferes with effective feature representation. To address this, this paper proposes a denoising wavelet transform convolution (DWTConv) and constructs the DWTPred-Net model with it as the key component. To systematically validate the model’s performance, we compared it with mainstream models (C1PG, ConvLSTM, and ConvGRU) under different solar activity conditions. The results show that both

M A E

and

R M S E

of DWTPred-Net are greatly reduced under all test conditions. In high solar activity, DWTPred-Net reduces

R M S E

by 13.81%, 6.19%, and 9.28% compared to the C1PG, ConvLSTM, and ConvGRU, respectively. In low solar activity, the advantage of DWTPred-Net becomes even more pronounced, with

R M S E

reductions further increasing to 19.39%, 11.51%, and 16.10%, respectively. Furthermore, in additional tests across different latitudinal bands and during geomagnetic storm events, the model consistently demonstrates superior performance. These multi-perspective experimental results collectively indicate that DWTPred-Net possesses obvious advantages in improving TEC prediction accuracy. Full article

(This article belongs to the Section Upper Atmosphere)

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16 pages, 8313 KB

Open AccessArticle

Evaluation of WRF Planetary Boundary Layer Parameterization Schemes for Dry Season Conditions over Complex Terrain in the Liangshan Prefecture, Southwestern China

by Jinhua Zhong, Debin Su, Zijun Zheng, Wenyu Kong, Peng Fang and Fang Mo

Atmosphere 2026, 17(1), 53; https://doi.org/10.3390/atmos17010053 - 31 Dec 2025

Abstract

The planetary boundary layer (PBL) exerts strong control on heat, moisture, and momentum exchange, yet its representation over the steep mountains and deep valleys of Liangshan remains poorly understood. This study evaluates six Weather Research and Forecasting (WRF) PBL schemes (ACM2, BL, MYJ, [...] Read more.

The planetary boundary layer (PBL) exerts strong control on heat, moisture, and momentum exchange, yet its representation over the steep mountains and deep valleys of Liangshan remains poorly understood. This study evaluates six Weather Research and Forecasting (WRF) PBL schemes (ACM2, BL, MYJ, MYNN2.5, QNSE, and YSU) using multi-source observations from radiosondes, surface stations, and wind profiling radar during clear-sky dry-season cases in spring and winter. The schemes exhibit substantial differences in governing turbulent mixing and stratification. For the specific cases studied, QNSE best reproduces 2 m temperature in both seasons by realistically capturing nocturnal stability and large diurnal ranges, while non-local schemes overestimate nighttime temperatures due to excessive mixing. MYNN2.5 performs robustly for boundary layer growth in spring, and BL aligns most closely with radar-derived PBL height (PBLH). Vertical profile comparisons show that QNSE and MYJ better represent the lower–middle level thermodynamic structure, whereas all schemes underestimate extreme near-surface winds, reflecting unresolved terrain-induced variability. PBLH simulations reproduce diurnal cycles but differ in amplitude, with QNSE occasionally producing unrealistic spikes. Overall, no scheme performs optimally for all variables. However, QNSE and MYNN2.5 show the most balanced performance across seasons. These findings provide guidance for selecting PBL schemes for high-resolution modeling and fire–weather applications over complex terrain. Full article

(This article belongs to the Special Issue Meteorological Models: Recent Trends, Current Progress and Future Directions (2nd Edition))

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14 pages, 1776 KB

Open AccessArticle

Theoretical Computation-Driven Screening and Mechanism Study of Washing Oil Composite Solvents for Benzene Waste Gas Absorption

by Chengyi Qiu, Zekai Jin, Meisi Chen, Li Wang, Sisi Li, Gang Zhang, Muhua Chen, Xinbao Zhu and Bo Fu

Atmosphere 2026, 17(1), 52; https://doi.org/10.3390/atmos17010052 - 31 Dec 2025

Abstract

In order to solve the problems of high volatility and insufficient absorption effect when using chemical by-product washing oil to treat benzene-containing waste gas, this study innovatively proposed a composite solvent screening method based on the solvation free energy (ΔGsol), and [...] Read more.

In order to solve the problems of high volatility and insufficient absorption effect when using chemical by-product washing oil to treat benzene-containing waste gas, this study innovatively proposed a composite solvent screening method based on the solvation free energy (ΔGsol), and reasonably predicted the absorption performance of 26 solvents for benzene. Through theoretical calculation and experimental verification, tetraethylene glycol dimethyl ether (TGDE) was finally determined to be the optimal composite component of washing oil. The absorption efficiency of the composite solvent reached 96.2%, and the regeneration efficiency was stable after 12 cycles with a mass loss of only 2.4%. Quantum computing simulation revealed that the dispersion force is dominant between benzene and the solvent, and TGDE enhances the electrostatic interaction through weak hydrogen bonds. The synergistic effect of the two improves the absorption performance. This study provides theoretical and technical support for the development of efficient and renewable benzene waste gas recovery solvent systems. Full article

(This article belongs to the Section Air Pollution Control)

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21 pages, 10371 KB

Open AccessArticle

Constrained Estimates of Anthropogenic NO_x Emissions in China (2014–2021) from Surface Observations

by Yang Shen, Shuzhuang Feng, Zihan Yang, Chenchen Peng, Guoen Wei and Yuanyuan Yang

Atmosphere 2026, 17(1), 51; https://doi.org/10.3390/atmos17010051 - 31 Dec 2025

Abstract

China’s rapid urbanization has precipitated severe atmospheric pollution, drawing sustained scientific and policy attention. Although nationwide implementations of emission control measures have achieved measurable reductions in ambient NO₂ concentrations, fundamental uncertainties persist in quantifying anthropogenic NO_x emission and their interannual variability. [...] Read more.

China’s rapid urbanization has precipitated severe atmospheric pollution, drawing sustained scientific and policy attention. Although nationwide implementations of emission control measures have achieved measurable reductions in ambient NO₂ concentrations, fundamental uncertainties persist in quantifying anthropogenic NO_x emission and their interannual variability. In this study, NO_x emissions over China are inferred using the Regional Air Pollutant Assimilation System (RAPAS) combined with ground-based hourly NO₂ observations, and a detailed analysis of the spatiotemporal variation patterns of NO_x emissions is also provided. Nationally, most sites display declining NO₂ concentrations during 2014–2021, with steeper reduction trends in winter, particularly in pollution hotspots. The RAPAS-optimized NO_x emission estimates demonstrate superior performance relative to prior inventories, with site-averaged biases, root mean square errors, and correlation coefficients improved substantially across all geographic regions in China. The trajectories of changes in NO_x emissions exhibit marked regional disparities: South and Northeast China experienced more than 8.0% emission growth during 2014–2017, while NO_x emissions in northwest and southwest China increased by 35% and 26%, significantly higher than those in East China. The reductions accelerated significantly post 2018, particularly in central and eastern regions (more than −20%). The interannual variation in NO_x emissions in the five national urban agglomerations shows a similar trend of first rising and then decreasing. The NO_x emissions of Anhui, Yunnan, Shanxi, Gansu and Xinjiang provinces increased significantly from 2014 to 2017, while the emissions of Shandong and Zhejiang decreased at a relatively high rate (more than 80 Gg per year). These findings are helpful to provide a more comprehensive understanding of current NO_x pollution and provide scientific basis for policymakers to propose effective strategies. Full article

(This article belongs to the Special Issue Emission Inventories and Modeling of Air Pollution)

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19 pages, 6979 KB

Open AccessArticle

Los Angeles Wildfires 2025: Satellite-Based Emissions Monitoring and Air-Quality Impacts

by Konstantinos Michailidis, Andreas Pseftogkas, Maria-Elissavet Koukouli, Christodoulos Biskas and Dimitris Balis

Atmosphere 2026, 17(1), 50; https://doi.org/10.3390/atmos17010050 - 31 Dec 2025

Abstract

In January 2025, multiple wildfires erupted across the Los Angeles region, fueled by prolonged dry conditions and intense Santa Ana winds. Southern California has faced increasingly frequent and severe wildfires in recent years, driven by prolonged drought, high temperatures, and the expanding wildland–urban [...] Read more.

In January 2025, multiple wildfires erupted across the Los Angeles region, fueled by prolonged dry conditions and intense Santa Ana winds. Southern California has faced increasingly frequent and severe wildfires in recent years, driven by prolonged drought, high temperatures, and the expanding wildland–urban interface. These fires have caused major loss of life, extensive property damage, mass evacuations, and severe air-quality decline in this densely populated, high-risk region. This study integrates passive and active satellite observations to characterize the spatiotemporal and vertical distribution of wildfire emissions and assesses their impact on air quality. TROPOMI (Sentinel-5P) and the recently launched TEMPO geostationary instrument provide hourly high temporal-resolution mapping of trace gases, including nitrogen dioxide (NO₂), carbon monoxide (CO), formaldehyde (HCHO), and aerosols. Vertical column densities of NO₂ and HCHO reached 40 and 25 Pmolec/cm², respectively, representing more than a 250% increase compared to background climatological levels in fire-affected zones. TEMPO’s unique high-frequency observations captured strong diurnal variability and secondary photochemical production, offering unprecedented insights into plume evolution on sub-daily scales. ATLID (EarthCARE) lidar profiling identified smoke layers concentrated between 1 and 3 km altitude, with optical properties characteristic of fresh biomass burning and depolarization ratios indicating mixed particle morphology. Vertical profiling capability was critical for distinguishing transported smoke from boundary-layer pollution and assessing radiative impacts. These findings highlight the value of combined passive–active satellite measurements in capturing wildfire plumes and the need for integrated monitoring as wildfire risk grows under climate change. Full article

(This article belongs to the Special Issue Advances in Integrated Air Quality Management: Emissions, Monitoring, Modelling (4th Edition))

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27 pages, 2814 KB

Open AccessArticle

Modeling the Start of Season Date of Hungarian Grasslands Using Remote Sensing Data and 10 Process-Based Models

by Réka Ágnes Dávid, Zoltán Barcza, Roland Hollós and Anikó Kern

Atmosphere 2026, 17(1), 49; https://doi.org/10.3390/atmos17010049 - 30 Dec 2025

Abstract

Vegetation phenology, particularly the start of the growing season (SOS) date, is a key indicator of the climate sensitivity of ecosystems, yet its accurate prediction remains challenging. This study investigates the SOS of Hungarian grasslands between 2000 and 2023 using MODIS NDVI data, [...] Read more.

Vegetation phenology, particularly the start of the growing season (SOS) date, is a key indicator of the climate sensitivity of ecosystems, yet its accurate prediction remains challenging. This study investigates the SOS of Hungarian grasslands between 2000 and 2023 using MODIS NDVI data, testing ten process-based models of varying complexity. Model parameters were optimized with the differential evolution algorithm under three calibration strategies: generic (GEN, aiming for a single model setting for the country), grassland-type (GEN GRASS, where grasslands are first categorized and then a type-specific parameterization is sought), and pixel-level (PIX, where model parameterization is performed for each pixel separately). The models with the lowest RMSE values were AGSI and AHSGSI (driven by temperature, vapor pressure deficit, and photoperiod) under PIX (RMSE = 3.3 days), AGSIwSW (driven by temperature, soil water content, and photoperiod) under GEN and GEN GRASS (RMSE = 7.6 and 6.3 days, respectively), and MGDDwPP (driven by temperature and photoperiod) under GEN (RMSE = 7.6 days). Considering the Akaike Information Criteria, the simplest GDD model (driven by temperature only) was the proposed one under PIX, while MGDDwPP was identified as the best model both in GEN and GEN GRASS. Residual analysis revealed relatively strong co-variation between model errors and some basic climate anomalies (most of all spring temperature and soil water content), enabling statistical corrections that reduced bias close to zero across all models. Integrating local climate and soil information into phenology models enhances their accuracy for grassland SOS estimation in Central Europe. Full article

(This article belongs to the Section Biometeorology and Bioclimatology)

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17 pages, 6494 KB

Open AccessArticle

Wide-Spectral-Range, Multi-Directional Particle Detection by the High-Energy Particle Detector on the FY-4B Satellite

by Qingwen Meng, Guohong Shen, Chunqin Wang, Qinglong Yu, Lin Quan, Huanxin Zhang and Ying Sun

Atmosphere 2026, 17(1), 48; https://doi.org/10.3390/atmos17010048 - 30 Dec 2025

Abstract

The FY-4B satellite, launched in June 2021 as China’s new-generation geostationary meteorological satellite, carries three identical High-Energy Particle Detectors (HEPDs) that enable multi-directional, wide-spectral measurements of energetic electrons. The three units are mounted in the zenith (−Z), flight (+X with a +Y offset [...] Read more.

The FY-4B satellite, launched in June 2021 as China’s new-generation geostationary meteorological satellite, carries three identical High-Energy Particle Detectors (HEPDs) that enable multi-directional, wide-spectral measurements of energetic electrons. The three units are mounted in the zenith (−Z), flight (+X with a +Y offset of 30°), and anti-flight (−X with a −Y offset of 30°) directions, allowing simultaneous observations from nine look directions over a field of view close to 180° in the 0.4–4 MeV energy range (eight energy channels). This paper systematically presents the design principles of the HEPD electron detector, the ground calibration scheme, and preliminary in-orbit validation results. The probe employs a multi-layer silicon semiconductor telescope technique to achieve high-precision measurements of electron energy spectra, fluxes, and directional anisotropy in the 0.4–4 MeV range. Ground synchrotron calibration shows that the energy resolution is better than 16% for energies above 1 MeV, and the angular resolution is about 20°, providing a solid basis for subsequent quantitative inversion. During in-orbit operation, HEPD remains stable under both quiet conditions and strong geomagnetic storms: the measured electron fluxes, differential energy spectra, and directional distributions show good agreement with GOES-16 observations in the same energy bands during quiet periods and for the first time provide from geostationary orbit pitch-angle-resolved images of the minute-scale evolution of electron enhancement events. These results demonstrate that HEPD is capable of long-term monitoring of the geostationary radiation environment and can supply high-quality, continuous, and reliable data to support studies of radiation-belt particle dynamics, data assimilation in space weather models, and radiation warnings for satellites in orbit. Full article

(This article belongs to the Section Upper Atmosphere)

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19 pages, 3284 KB

Open AccessFeature PaperArticle

Understanding Motorcycle Emissions Across Their Technical, Behavioral, and Socioeconomic Determinants in the City of Kigali: A Non-Parametric Multivariate Analysis

by Gershome G. Abaho, Bernard B. Munyazikwiye, Hussein Bizimana, Jacqueline Nikuze, Moise Ndekezi, Jean de Dieu Mutabaruka, Donald Rukotana Kabanda, Maximillien Mutuyeyezu, Telesphore Habiyakare, Emmanuel Tuyizere, Thomas Matabaro, Prince Bonfils Bimenyimana and Gilbert Nduwayezu

Atmosphere 2026, 17(1), 47; https://doi.org/10.3390/atmos17010047 - 30 Dec 2025

Abstract

Motorcycle emissions are a growing environmental and public health concern in many low- and middle-income countries. While several studies have examined the emission profiles from larger vehicles in urban areas, very few have analyzed motorcycle emissions through a parametric and non-parametric multivariate lens, [...] Read more.

Motorcycle emissions are a growing environmental and public health concern in many low- and middle-income countries. While several studies have examined the emission profiles from larger vehicles in urban areas, very few have analyzed motorcycle emissions through a parametric and non-parametric multivariate lens, combining technical, behavioral, and socioeconomic factors, a gap that this study attempts to address. MANOVA and Kruskal–Wallis H test analyses highlighted visible smoke emissions (hydrocarbon: H = 30.62, p < 0.001; carbon monoxide: H = 16.71, p < 0.001; dioxygen: H = 6.67, p = 0.010), year of manufacturing (carbon monoxide: H = 20.61, p < 0.001; hydrocarbon: H = 11.80, p = 0.008), average fuel consumption (carbon dioxide: H = 13.32, p = 0.004), and daily driving distance (carbon monoxide: H = 10.09, p = 0.018) as significant predictors of emissions. The results also indicate that newer and well-maintained motorcycles (2018–2021) consistently showed the lowest carbon monoxide and HC levels compared to the older and poorly maintained counterparts with the highest emissions. Consistently, motorcycles with visible smoke showed substantially elevated carbon monoxide and hydrocarbons and reduced dioxygen, establishing visible smoke as a practical marker for excessive emissions. Additionally, younger riders (19–28 years) exhibited higher hydrocarbon emissions, while greater riding experience and more passengers influenced dioxygen levels. Spearman correlation analysis reinforced these patterns, with visible smoke showing strong positive correlations with carbon monoxide (ρ = 0.21) and hydrocarbon (ρ = 0.28), and carbon monoxide was negatively associated with motorcycle age (ρ = −0.18). These findings underscore manufacturing year, vehicle maintenance, and visible smoke as practical, high-impact targets for reducing motorcycle emissions, offering a basis for targeted emission control strategies. Full article

(This article belongs to the Section Air Quality)

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24 pages, 5359 KB

Open AccessArticle

Fire and the Vulnerability of the Caatinga Biome to Droughts and Heatwaves

by Katyelle F. S. Bezerra, Helber B. Gomes, Janaína P. Nascimento, Dirceu Luís Herdies, Hakki Baltaci, Maria Cristina L. Silva, Gabriel de Oliveira, Erin Koster, Heliofábio B. Gomes, Madson T. Silva, Fabrício Daniel S. Silva, Rafaela L. Costa and Daniel M. C. Lima

Atmosphere 2026, 17(1), 46; https://doi.org/10.3390/atmos17010046 - 29 Dec 2025

Abstract

This study analyzes the relationship between fires and climate extremes in the Caatinga biome from 2012 to 2023 by integrating Fire Radiative Power (FRP) from VIIRS (S-NPP and NOAA-20), Vapor Pressure Deficit (VPD) and air temperature from ERA5, drought indices (SPI-1 and SPI-6), [...] Read more.

This study analyzes the relationship between fires and climate extremes in the Caatinga biome from 2012 to 2023 by integrating Fire Radiative Power (FRP) from VIIRS (S-NPP and NOAA-20), Vapor Pressure Deficit (VPD) and air temperature from ERA5, drought indices (SPI-1 and SPI-6), and heatwave events from the Xavier database. Daily percentiles of maximum (CTX90pct) and minimum (CTN90pct) temperatures were used to characterize heatwaves. Spatial and temporal dynamics of fire patterns were identified using the HDBSCAN algorithm, an unsupervised Machine Learning clustering method applied in three-dimensional space (latitude, longitude, and time). A marked seasonality was observed, with fire activity peaking from August to November, especially in October, when FRP reached ~1000 MW/h. The years 2015, 2019, 2021, and 2023 exhibited the highest fire intensities. A statistically significant upward trend in cluster frequency was detected (+1094.96 events/year; p < 0.001). Cross-correlations revealed that precipitation deficits (SPI) preceded FRP peaks by about four months, while VPD and air temperature exerted immediate positive effects. FRP correlated positively with heatwave frequency (r = 0.62) and negatively with SPI (r = −0.69). These findings highlight the high vulnerability of the Caatinga to compound drought and heat events, indicating that fire management strategies should account for both antecedent drought conditions, monitored through SPI, and real-time atmospheric dryness, measured by VPD, to effectively mitigate fire risks. Full article

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

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55 pages, 2510 KB

Open AccessArticle

Concentration of PM_2.5 and PM₁₀ Particulate Matter in Various Indoor Environments: A Literature Review

by Angelika Baran and Ewa Zender-Świercz

Atmosphere 2026, 17(1), 45; https://doi.org/10.3390/atmos17010045 - 29 Dec 2025

Abstract

Indoor exposure to particulate matter (PM_2.5 and PM₁₀) remains a significant public health problem, especially in high-traffic areas, where outdoor pollution, building characteristics, and user activity jointly influence indoor air quality. This study aims to synthesise and compare the effectiveness [...] Read more.

Indoor exposure to particulate matter (PM_2.5 and PM₁₀) remains a significant public health problem, especially in high-traffic areas, where outdoor pollution, building characteristics, and user activity jointly influence indoor air quality. This study aims to synthesise and compare the effectiveness of key technical solutions to reduce indoor PM concentrations in different types of buildings. A comprehensive review and comparative analysis of published experimental and field studies were conducted, covering residential, educational, office, medical, sports, and heritage buildings. The interventions evaluated included mechanical ventilation and filtration systems, portable HEPA air cleaners, integrated building envelope solutions, airflow optimisation strategies, and selected auxiliary technologies. Reported performance metrics such as baseline indoor and outdoor PM concentrations, air exchange rate (ACH), filter class, clean air delivery rate (CADR), and percentage reduction were systematically analysed. The results indicate that mechanical filtration, particularly high-efficiency HVAC (Heating Ventilation and Air-Conditioning) systems and HEPA filters, provide the most reliable and repeatable reductions in PM_2.5 and PM₁₀, especially under controlled airflow and recirculation conditions. Integrated approaches that combine airtight building envelopes, mechanical ventilation, and local air purification achieved the highest overall effectiveness. The findings confirm that successful PM mitigation requires context-specific multicomponent strategies tailored to building type, outdoor pollution load, occupancy, and ventilation design. Full article

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

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13 pages, 4674 KB

Open AccessArticle

Interpretational Pitfalls in SOM-Based Clustering: A Case Study of Extreme Cold Events in South Korea

by Jae-Seung Yoon, Sunmin Park and Il-Ung Chung

Atmosphere 2026, 17(1), 44; https://doi.org/10.3390/atmos17010044 - 29 Dec 2025

Abstract

Understanding the physical mechanisms of extreme weather and climate events often relies on identifying typical large-scale circulation patterns associated with such extremes. Self-organizing maps (SOMs) have therefore been widely applied in atmospheric and climate studies as an objective clustering tool for circulation pattern [...] Read more.

Understanding the physical mechanisms of extreme weather and climate events often relies on identifying typical large-scale circulation patterns associated with such extremes. Self-organizing maps (SOMs) have therefore been widely applied in atmospheric and climate studies as an objective clustering tool for circulation pattern classification. However, because SOM necessarily assigns all events to a limited number of representative nodes, individual extreme events with atypical large-scale circulation patterns may be grouped into clusters that do not adequately represent their underlying dynamics. In this study, we examine this interpretational issue using 223 severe January cold events over South Korea during 1949–2021. We show that a substantial fraction of cold events exhibits weak or even conflicting similarity with their assigned SOM node patterns, indicating pronounced within-node heterogeneity. Although optimizing the SOM node configuration and cluster number reduces the proportion of atypical cases, such heterogeneity cannot be fully eliminated. We further apply a pattern-correlation–based post-processing approach (SOM-PC) to explicitly identify and exclude atypical cases, which reduces the number of atypical cold events by approximately 27%. Rather than pointing to limitations of SOM itself, our results underscore potential interpretational pitfalls that can arise when SOM-derived circulation patterns are directly linked to extreme events without evaluating the representativeness of individual cluster members. These findings highlight the importance of applying explicit diagnostics for within-cluster heterogeneity when using SOM or similar data-driven tools to elucidate large-scale circulation patterns underlying localized extreme weather and climate events. Full article

(This article belongs to the Section Climatology)

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2 pages, 336 KB

Open AccessCorrection

Correction: Yang et al. Perfluorinated and Polyfluoroalkyl Compounds in the Atmosphere: A Review. Atmosphere 2025, 16, 1070

by Haoran Yang, Ying Liang, Shili Tian, Xingru Li and Yanju Liu

Atmosphere 2026, 17(1), 43; https://doi.org/10.3390/atmos17010043 - 29 Dec 2025

Abstract

In the original publication [...] Full article

(This article belongs to the Section Air Quality)

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18 pages, 4920 KB

Open AccessArticle

Modeling of Drought-Induced Crop Yield Loss Based on Solar-Induced Chlorophyll Fluorescence by Machine Learning Methods

by Han Hu, Minxue Zheng, Yue Niu, Qiu Shen, Qinyao Ren and Yanlin You

Atmosphere 2026, 17(1), 42; https://doi.org/10.3390/atmos17010042 - 28 Dec 2025

Abstract

Against the accelerating backdrop of global warming, drought-induced crop yield loss not only causes direct economic losses but may also disrupt the dynamic balance of food production and consumption, ultimately threatening global food security. In order to quantify drought-induced crop yield loss for [...] Read more.

Against the accelerating backdrop of global warming, drought-induced crop yield loss not only causes direct economic losses but may also disrupt the dynamic balance of food production and consumption, ultimately threatening global food security. In order to quantify drought-induced crop yield loss for safeguarding national food security, this study developed a model for evaluating drought-induced yield reduction in winter wheat by integrating solar-induced chlorophyll fluorescence (SIF), vegetation indices (VIs), and meteorological data. The results demonstrated that the following: (1) SIF could effectively capture interannual fluctuations in winter wheat yield and serve as a reliable quantitative indicator of yield variation. (2) Utilizing vegetation data such as SIF and the near-infrared reflectance of vegetation (NIRv), the developed models could directly quantify drought-induced yield losses in winter wheat based on normalized anomalies of vegetation and meteorological variables, without the need for additional auxiliary data or complex computations. Among all variable combinations tested, SIF demonstrated superior performance, yielding the most accurate predictions. (3) Both random forest (RF) and extreme gradient boosting (XGBoost) algorithms had similar performance in evaluating drought-induced yield loss. The results highlighted the advantages of combining the normalized anomaly of multiple sources of data as inputs in stress-induced crop yield loss evaluation, which was helpful for quick monitoring and early warning of the crop yield loss in the major grain production region. Full article

(This article belongs to the Section Biometeorology and Bioclimatology)

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