Search Results (12,403)

Accurately quantifying carbon dioxide (CO₂) emissions from heavy-duty diesel trucks (HDTs) is crucial for developing effective transportation emission reduction strategies. In this study, we adopted a bottom–up approach and, in conjunction with the “International Vehicle Emissions” (IVE) model, constructed a high-resolution 1 × 1 km CO₂ emission inventory for the urban area of Kunming, China. Using data from 1.24 million track points collected from 5996 heavy-duty diesel trucks, we implemented a map matching algorithm based on a simplified hidden Markov model (HMM) to efficiently process large-scale GPS data. Furthermore, we improved upon traditional spatial allocation methods by dynamically integrating track point density with static road network density. The results indicate that although higher driving speeds correspond to lower CO₂ emission rates, heavy-duty diesel trucks typically operate within an observed speed range of 40–60 km/h, with an average emission factor of approximately 500 g/km. Vehicles compliant with the “National III” emission standards remain the primary source of CO₂ emissions in this region. Correlation analysis reveals a significant positive relationship (p < 0.01) between emissions from heavy-duty diesel trucks and both traffic volume and mileage. Notably, daytime vehicle restriction policies led to a temporal redistribution of emissions rather than a net reduction in emissions; this resulted in increased activity levels of heavy-duty diesel trucks at night, leading to a surge in nighttime emissions. In terms of spatial distribution, the “dual-density” allocation method proposed in this study more accurately captured emission hotspots, revealing that CO₂ emissions are primarily concentrated in the southeastern part of the city—a distribution pattern largely influenced by the city’s industrial layout. Full article

Volatile organic compounds (VOCs) are key precursors of ozone (O₃) and secondary organic aerosols (SOA), among which biogenic VOCs (BVOCs) constitute the dominant natural source. However, large uncertainties remain in the magnitude, spatial distribution, and seasonal variability of BVOC emissions in China under rapidly changing vegetation and climate conditions. In this study, a refined BVOC emission inventory for China in 2023 was developed using the Model of Emissions of Gases and Aerosols from Nature (MEGAN v3.2) driven by WRF meteorological simulations and MODIS vegetation data. The estimated annual BVOC emissions reached 41.70 Tg, including 26.90 Tg isoprene, 4.84 Tg monoterpenes, 0.55 Tg sesquiterpenes, and 9.41 Tg other VOCs. The corresponding ozone formation potential (OFP) and secondary organic aerosol formation potential (SOAFP) were 346.12 Tg yr⁻¹ and 2137.51 Gg yr⁻¹, respectively. Emissions exhibited a pronounced south–north gradient with hotspots in Guangxi, Guangdong, and Yunnan, and peaked in summer. Broadleaf forests were identified as the dominant emission sources, followed by savannas and shrublands. Isoprene contributed most to OFP, whereas monoterpenes dominated SOAFP. Compared with previous inventories, the updated vegetation data, meteorological inputs, and refined chemical speciation improve the representation of BVOC emissions and their spatial patterns in China. These results highlight the important role of BVOCs in regional O₃ and SOA formation and provide an improved emission basis for atmospheric chemistry modeling and air-quality management. Full article

We analyze the evolution of atmospheric and surface physical properties over the region of the Earth observed by the Meteosat Second Generation (MSG) satellites during the period 2005–2024. Long-term changes are detected in the observed radiances, with a decrease in the solar domain (−1.3%) and an increase in the thermal infrared domain (+0.4%), consistent with trends reported by independent broadband radiometers such as CERES. The outgoing solar radiance (OSR) exhibits a marked decline, which we associate with a reduction in low-level cloud cover within the nominal Meteosat field of view (MFoV) centered at 0° longitude. Changes in atmospheric CO₂ concentration also contribute to the observed radiative imbalance at the top of the atmosphere (TOA). Instrument calibration stability and inter-satellite homogenization across the MSG series are explicitly addressed, enabling the detection of robust interdecadal signals. By subdividing the MFoV into 60 regional sectors, we characterize spatial variations in cloud amount at low and high atmospheric levels and relate these changes to regional TOA radiative imbalances and concurrent variations in Atlantic sea surface temperature (SSTs). The spectral information provided by SEVIRI allows a more detailed attribution of radiative changes than broadband observations alone from other instruments. In particular, radiances measured in the atmospheric split-window region near 11 µm are shown to be sensitive to variations in low-tropospheric humidity, which exhibits a widespread decadal-scale increase. The results indicate a close coupling between cloud-cover changes, radiative fluxes, and SST evolution on the recent interdecadal time scale. The observed decrease in low-level total cloud cover is independently in line with ECMWF ERA5 reanalysis data. These findings highlight the value of long, stable geostationary observations for investigating atmosphere–ocean interactions and their role in regional climate variability. Full article

Rills on the snow surface are a common phenomenon frequently reported by field observers. The interpretation of these field observations and an understanding of the underlying physical processes are important for forecasting routines and models used in avalanche warning as well as in hydrological and meteorological forecasting. Rills on the snow surface are typically associated with rain-on-snow (ROS) events and are often interpreted as an indicator of the approximate snowfall level. However, recent field observations of rills on the snow surface without significant liquid precipitation in the Austrian Alps challenge the assumption that ROS events are the sole cause of rill formation. In this study, we quantitatively compare liquid water input into the snowpack from melt processes to the amount of rain during a documented rill formation event. Using a combination of field observations, energy balance calculations, and model simulations, our results strongly suggest that, in this case study, meltwater was the predominant source of liquid water input and snowmelt the main driver of rill formation. Our results indicate that more than 97% of the total liquid water input originated from melt, while rain contributed only roughly 2%. These findings highlight the need for a revised interpretation of rill formation, suggesting that meltwater-driven rills may be more significant than previously assumed. Full article

The hydrocarbon composition of liquefied PM1 aerosol samples collected using the particle into liquid system (PILS) at the Atmospheric Radiation Measurement (ARM) site of the Southern Great Plains (SGP) of the USA was analyzed in terms of organic compound composition. The results indicate that anthropogenic aliphatic compounds contributed significantly to the organic pool of PM1 fine aerosols in the ambient air of the rural area of the Southern Great Plains, with a broad range of aliphatic hydrocarbons (HCs) being the dominant organic component. The molecular markers of hopanes and steranes were generally absent or present in trace amounts in most samples, but a significant number of low-abundance hopanes and steranes were detected in only two samples, while the aromatic compounds were generally insignificant and comprised mainly low molecular weight naphthalene and its methylated derivatives. The overall composition of organic compounds and the back trajectories analysis for the sampling days suggest that the local petroleum refinery and vehicular emissions are the two major sources of the aliphatic and aromatic compounds in the fine aerosols, while plant wax may occasionally contribute a minor portion of organic matter. Furthermore, it was found that the organic composition of PM1 fine aerosol was clearly related to the ambient air temperature and suggests that the temperature is a controlling factor of organic aerosol formation. Full article

Accurate prediction of reference evapotranspiration (ET₀) using integrated deep learning approaches with limited meteorological data is highly significant for efficient water resource utilization and management in arid regions. Nevertheless, parameter optimization is frequently overlooked in current research, leading to unsatisfactory estimation accuracy that cannot meet practical application requirements. To overcome this limitation, a CNN–BiLSTM–attention hybrid model is constructed by combining the powerful feature-extraction capability of CNN and excellent sequence-processing performance of BiLSTM, followed by the integration of an attention mechanism. Five metaheuristic algorithms, namely the osprey optimization algorithm (OOA), grey wolf optimization (GWO), whale optimization algorithm (WOA), particle swarm optimization (PSO), and northern goshawk optimization (NGO), are adopted to optimize the key parameters of the proposed model. The developed hybrid models are then applied to ET₀ estimation in Linze County, China. The results demonstrate that the error indices of these models vary within the ranges of MAPE [14.28%, 14.48%], MAE [0.4270, 0.4482], RMSE [0.5596, 0.5844], and NMSE [0.0490, 0.0577]. Overall, the OOA–CNN–BiLSTM–attention model exhibited the most robust and consistent estimation performance across multiple evaluation metrics among the investigated models. Full article

The study is based on Land Surface Temperature (LST) and Air Temperature data and Nonparametric Data Envelopment Analysis (DEA) technique to evaluate heat efficiency and detect anomalies in the thermal regime in the Eastern Black Sea Region, particularly in Hopa and Artvin, during the period 2000–2024. The regulating role of the Black Sea has resulted in Hopa having the warmest and most stable temperature patterns, with daytime temperatures 1.8 to 3.7 °C higher than Artvin. Previous DEA analysis of daytime temperatures has shown that the 2018–2020 period had the highest daily temperatures, while the 2001–2010 decade was characterized by the highest nighttime temperatures. A future heat map based on Monte Carlo simulation using six climate change scenarios indicates that in the most optimistic case, assuming a temperature increase of +0.8 °C, efficiency scores could increase as high as 0.995. On the other hand, if global warming leads to a sudden temperature increase above +7.2 °C, there is a 21.7% climate efficiency loss. Sensitivity analysis showed that technological innovation and good governance are the main positive factors affecting climate efficiency. Random Forest (RF) and SHapley Additive Explanations (SHAP) analyses were applied to determine the impact of climate factors on DEA scores and also indicated areas requiring risk assessment. The findings highlight the importance of considering location-specific climate adaptation strategies. Based on the observed thermal contrasts between coastal and inland environments, potential adaptation considerations may include urban heat management and agricultural water stress in coastal areas such as Hopa, and cold-climate resilience and energy-efficient infrastructure in inland locations such as Artvin. Full article

Based on the Advanced Geostationary Radiation Imager (AGRI) and Geostationary High-speed Imager (GHI) information in the Fengyun-4B (FY-4B) satellite, we propose a convective initiation (CI) nowcasting algorithm for Sichuan Province, China. The algorithm optimizes satellite reflectance by considering multi-channel brightness differences, visible reflectance, and cloud-top cooling by exploiting the Farneback optical flow, where the cloud is followed by false cooling due to cloud motion. Moreover, the high temporal resolution of GHI enables the detection of early cumulus cloud growth. The algorithm was developed using daytime CI events in the coverage area of Mianyang radar station from 22 July to 9 August 2023, and the remaining areas in the Chengdu scan area were used for validation. The results showed that the proposed method achieves a probability of detection (POD) of 83.1%, a false alarm ratio (FAR) of 33.0%, and a critical success index (CSI) of 58.9%. Compared with the AGRI-only method and the SATCAST algorithm, the POD increases by 5.4% and 8.4%, respectively, while the CSI improves by 1.3% and 2.3%. The average lead time reaches 34.2 min, which is 4.6 min longer than AGRI-only and 7.9 min longer than SATCAST. This suggests that AGRI and GHI data improve the spatiotemporal resolution of CI nowcasting. This approach improves the early detection of convective initiation under the climatic background of warm cloud convection in Sichuan, offering new insights for short-term warnings of regional convective weather. Full article

This study investigates whether a university teaching complex equipped with CSA S600 continuous air purification and sanitation units can maintain indoor air quality (IAQ) within recommended thresholds under real occupancy conditions and evaluates the impact of renovation works on IAQ. The work provides the first real-world assessment of the CSA S600 integrated monitoring system in an academic environment. CO₂, PM_2.5, PM₁₀ and VOCs were continuously measured over three months; moreover, indoor PM₁₀ values were compared with outdoor data from the regional monitoring network. Indoor CO₂ generally remained below 800 ppm, with short peaks of 1000–1500 ppm during high occupancy. PM_2.5 and PM₁₀ consistently stayed below the latest WHO guidelines, showing uniform recurring temporal patterns overtime; furthermore, indoor PM₁₀ showed limited coupling with outdoor trends, indicating the predominance of internal sources and ventilation dynamics. After renovation of the main Lecture Hall, particulate levels remained low, while VOCs showed a modest increase attributable to new materials. Overall, the findings demonstrate that the CSA S600 system effectively supports healthy IAQ in educational settings and that continuous monitoring is essential for managing occupancy-driven fluctuations and assessing the effects of structural interventions. Full article

This study reconstructs annual streamflow variability in the Yenice Stream Basin (northwestern Türkiye) for the period 1809–2020 using tree-ring data, substantially extending the short instrumental record (1979–2020). Three moisture-sensitive conifer chronologies were integrated using principal component analysis (PCA), and the first two principal components were employed as predictors in a multiple linear regression model calibrated against observed streamflow. The model explains a significant proportion of interannual variability (R² = 0.39; adjusted R² = 0.36; p < 0.001). Temporal stability was assessed using a 30-year moving-window correlation analysis, which reveals consistently positive and statistically significant relationships across all subperiods, indicating a stable and persistent calibration relationship through time. Years exceeding ±1 standard deviation account for approximately 24% of the record, while extreme events (±2 standard deviations) represent about 5%. The reconstruction identified several extreme events, including severe drought years (e.g., 1840, 1887, and 1907) and extremely wet years (e.g., 1896 and 1936). Among these, 1887 stands out as one of the most severe drought years, while the period 1927–1928 represents a persistent low-flow episode. The reconstruction provides a long-term perspective on streamflow variability and contributes baseline information for regional water resource planning and hydroclimatic risk assessment. Full article

Conventional Heating, Ventilation, and Air Conditioning (HVAC) control systems primarily rely on environmental and physiological parameters, largely ignoring the critical influence of psychological states on thermal comfort. Overlooking this factor often leads to suboptimal occupant satisfaction, energy inefficiency and thus carbon dioxide (CO₂) emissions. To this aim, this study introduces a novel mood-adaptive HVAC control system integrating psychological feedback to decrease CO₂ emissions in office buildings by reducing energy consumption and optimizing comfort. A total of 7000 thermal facial measurement records and high-resolution camera images were collected across seven mood state conditions using video stimuli and the Profile of Mood States (POMS) questionnaire to evaluate mood variations. A dual artificial intelligence system was developed: a Convolutional Neural Network (CNN) for analyzing facial expressions and an Artificial Neural Network (ANN) for processing facial temperatures via thermal imaging. These models collectively predict occupant mood in real-time, and a custom-designed wearable necklace interface transmits this data to dynamically adjust HVAC setpoints. To evaluate system performance, energy consumption was directly measured in real-life operations using an energy analyzer, without relying on simulations. Results indicate that this prototype personalized mood-driven system has the potential to enhance perceived thermal comfort while achieving up to a 20% reduction in carbon emissions compared to conventional systems. This human-centered approach significantly advances intelligent building management and climate change mitigation. Full article

Baroclinic instability is a fundamental mechanism of midlatitude atmospheric variability, and the Eady model remains one of its most useful idealized representations. In this work, we revisit the Eady configuration from the viewpoint of solution-space geometry rather than the classical normal-mode/growth-rate analysis. Starting from the reduced Eady vertical-structure equation, we show that the ratio of two independent solutions satisfies a Schwarzian-type relation that is invariant under homographic transformations, which naturally leads to an SL(2R) projective symmetry of the solution family. On this basis, we introduce a complex amplitude representation and reformulate coherence in terms of phase–amplitude synchronization constrained by projective invariants. Using Riccati-type constructions along geodesic parametrizations, the reduced dynamics are connected to a Stoler-type transform. Numerical exploration of the reduced model shows a systematic dependence on the control parameter $\omega$: small $\omega$ is associated with simple oscillatory or burst-like behavior, intermediate $\omega$ with period-doubling-like behavior, and large $\omega$ with strongly modulated dynamics and more intricate reconstructed attractors. These results should be interpreted as properties of the reduced symmetry-based model, and they suggest that projective invariants may provide a useful framework for classifying organization regimes in Eady-type disturbances, complementary to classical growth-rate analyses. Full article

Dew constitutes a component of the near-surface water balance, but its large-scale fractal dynamical properties remain poorly documented across Africa. This study estimates dew amounts and investigates their fractal and multifractal behavior under African climatic conditions using gridded ERA5 datasets from 1993 to 2022. The Rescaled-Range (R/S) method, Multifractal Detrended Fluctuation Analysis (MFDFA), and the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) algorithm are used. Hurst exponent (Hu) and the multifractal spectrum width (ω) are evaluated at daily and monthly scales over the full period and two sub-periods (1993–2007 and 2008–2022). The results reveal pronounced spatial heterogeneity in dew distribution. Daily mean amounts range between 0 and 0.18 mm, corresponding to annual accumulations reaching up to ~85 mm·yr⁻¹ in humid coastal, equatorial, and sub-equatorial regions, while remaining below 0.5 mm·yr⁻¹ in hyper-arid deserts. The continental mean annual amount is ~35.5 mm·yr⁻¹. The Hurst exponent exhibits values between zero and one, indicating region-dependent persistent and anti-persistent behaviors. This suggests that prediction schemes based on preceding values may be suitable for dew time series prediction in African regions exhibiting persistent characteristics. The multifractal spectrum width (ω), reaching values of up to 10, highlights strong scaling heterogeneity, particularly at the monthly timescale. These findings indicate that African dew dynamics exhibit significant long-range dependence and multifractal variability, providing new insights into the intrinsic temporal structure of dew and into appropriate approaches for its forecasting. Full article

Port activities significantly alter local atmospheric chemistry, yet the nonlinear coupling mechanisms between nitrogen oxides (NO_x) and ozone (O₃) in these complex environments remain underexplored. In this study, MF-DFA and MF-DCCA were applied to explore the coupling dynamics between NO_x and O₃ in Hong Kong’s Kwai Chung port and Tap Mun non-port areas. Results indicate that while both pollutants exhibit multifractality, O₃ shows stronger persistence and scale-invariant complexity than NO_x (e.g., in the port area, spectral width Δα = 0.61 for O₃ vs. 0.40 for NO_x). Crucially, the non-port area demonstrates significantly stronger and more stable cross-correlations (with the cross-correlation Hurst exponent h_xy(2) = 0.85 and h_xy(q) ranging from 0.80 to 0.99) compared to the port area (h_xy(2) = 0.60, h_xy (q) ranging from 0.54 to 0.74). The weaker coupling in the port zone is attributed to the fact that intermittent factors such as ship emissions have disrupted the long-term memory of the system. The connections in non-port areas are stronger and more stable because they are less affected by local emissions and chemical processes. The cross-correlation exhibited obvious seasonal dependence, with the strongest multifractal intensity in summer (cross-multifractal Δα reaching up to 0.81) and the weakest in winter under the modulation of photochemical and meteorological conditions. These findings reveal that port-side pollution coupling is structurally more fragile and heterogeneous than the stable regional background, suggesting that effective air quality management requires strategies accounting for these cross-scale nonlinear dynamics. Full article

Due to the influence of multiple factors such as the physical and chemical properties of the atmosphere, the limitations of data sources, and the assumptions of inversion methods, there are many difficulties in inverting the concentration distribution with high temporal and spatial resolution over a large area near the ground. In this study, the GEOS-Chem chemical transport model was adopted. Through dynamic constraints of emission sources, meteorological fields, and chemical mechanisms, combined with the optimization output of radial basis functions, a 1 km × 1 km hourly near-surface nitrogen dioxide concentration distribution dataset in China from 2015 to 2024 was generated. Based on the analysis of spatial differences and temporal fluctuations, the concentration changes of nitrogen dioxide are closely related to human activities, climate change, and seasonal variations. Thanks to China’s implementation of a large number of proactive pollution control measures, the average annual concentration of nitrogen dioxide has dropped from 19.7 μg/m³ in 2015 to 14.1 μg/m³ in 2024, with a cumulative reduction of 28.43%. The phenomenon of the one-hour average concentration exceeding the limit of 200 μg/m³ has been basically eliminated across the country. Full article