Accurate AQI forecasting is essential for public health and environmental management. However, existing network models for AQI forecasting still exhibit limited predictive accuracy, with insufficient consideration of key influencing factors in current research. Therefore, we present a hybrid model, Transformer Encoder–CNN–BiLSTM. The model not only considers the influence of six major atmospheric pollutant factors (PM2.5, PM10, CO, NO2, SO2, O3), but also offers advantages in modeling long-range dependencies of time series, extracting local features and capturing periodicity and seasonal trends of AQI. Taking Shanghai, China as the research object, the R2, MAE and RMSE of the proposed model are 0.9781, 2.4266 and 4.0321 respectively, far superior to those of other comparison models. In the cross-city validation experiment, the AQI forecasting of Beijing, which has distinct climatic conditions from Shanghai while sharing the same national AQI standard and similar dominant pollutant structure, also demonstrates favorable performance with an R2 of 0.9712, and MAE and RMSE of 3.1275 and 6.6269 respectively. The results indicate that the model can effectively forecast the AQI of Chinese megacities with consistent AQI evaluation criteria.

Urban sustainable mobility requires understanding how people travel, which modes they use, and what impacts these choices generate. This study proposes a smart mobility analytics framework that integrates GPS traces, dynamic traffic variables, and machine learning to infer transport modes and sustainability metrics in Cuenca, Ecuador. Geospatial and kinematic data were collected at 1 Hz from 50 participants over four working weeks, yielding 8.99 million samples across five modes: walking, cycling, tram, bus, and private vehicles. A compact subset of physical and spatial predictors, derived from speed, acceleration, jerk, longitudinal forces, and distance to public transport routes, was selected using the Football Optimization Algorithm. A classification tree trained with a 70/15/15 train–validation–test split achieved an overall accuracy of 84.2%, with class precisions of about 99% for pedestrian and bicycle, 93% for tram, 76% for private vehicles, and 64% for bus. The classified trajectories show that walking and cycling account for approximately 65% of total travel time but only 2% of total distance and 1.7% of CO2 emissions, whereas motorized modes generate more than 98% of emissions. Buses contribute nearly four times more CO2 than private vehicles, despite carrying a larger passenger volume. The proposed framework delivers detailed, policy-relevant indicators to support low-carbon urban transport strategies.

In this study, a bench test was conducted employing the Worldwide Harmonized Light-duty Vehicles Test Cycle (WLTC) to investigate the emission rates of hydrocarbons (HCs), carbon monoxide (CO), and carbon dioxide (CO2) with two different gasolines and five gasoline vehicles. The results indicated that compared with X gasoline, X+ gasoline led to a reduction in the emission rates of HC, CO, and CO2, by 38%, 11%, and 7%, respectively, attributed to its lower aromatic hydrocarbon content, olefin content, and 90% evaporation temperature (T90), and higher oxygen content. X+ gasoline exhibited more emission reductions under both acceleration and deceleration conditions. The two gasolines showed consistent patterns: for X+ gasoline, the emission rates under acceleration conditions were significantly higher than those under deceleration conditions, by a factor of 14.9, 2.1, and 1.6 for HC, CO, and CO2, respectively. Stronger Spearman correlations between vehicle specific power (VSP) and the emission rates were observed at higher speed (>80 km/h) of X, than those at medium speed (40–80 km/h) and lower speed (≤40 km/h), for both gasolines. Overall, the grey relation analysis revealed obvious heterogeneity between each of the seven fuel properties (RON, T10, T50, T90, Oxygen content, Aromatics content, Olefin content) and each of the three emission rates. However, slightly higher relational degrees were observed between HC emissions and aromatics or olefin contents, highlighting the need for lowering aromatics and olefin contents, thus reducing HC emissions.

We evaluated the effect of ammonium sulfate, a major component of airborne particulate matter, in the quantification of airborne micro- and nanoplastics (AMNPs) by analytical pyrolysis–gas chromatography-mass spectrometry (Py-GC/MS). Analytical pyrolysis has shown promising potential in providing mass-based information on AMNPs, which are compatible with established standard protocols to monitor airborne particulate matter. Py-GC/MS can be performed with little to no sample preparation, minimizing the risk of polymer loss or sample contamination. However, reactive components of particulate matter, such as inorganic salts, can interfere with the Py-GC/MS measurement of polymers, leading to over/underestimation of the polymer content and instrument contamination. In this study, we have shown that ammonium sulfate can generate matrix interference in the quantification of AMNPs in PM2.5. We have provided a solution to this issue based on water rinsing of the particulate matter directly inside the pyrolysis crucible, avoiding sample loss and preventing instrument contamination.

Virtual impactors are widely used for particulate matter (PM) classification due to their advantages of small cut-off particle size, simple structural design, ease of operation, and high particle handling capability, enabling subsequent analysis based on the desired aerodynamic diameter. Existing studies have mainly focused on the effects of particle size and structural parameters on classification performance, whereas systematic investigations into the regulatory mechanisms of fluid medium properties and ambient temperature variations on cut-off particle size remain relatively limited. Particularly under low-temperature gas conditions, variations in gas dynamic viscosity may significantly influence the dynamics of inertial particle separation, thereby altering the classification performance of virtual impactors. In this study, a low-temperature carbon dioxide-driven virtual impactor is proposed. By regulating the physicochemical properties of low-temperature gas, effective control over the particle inertial separation process is achieved, thereby expanding the tunable range of classification performance in virtual impactors. Numerical simulation results indicate that under low-temperature CO2 conditions, the virtual impactor can achieve a cut-off particle size classification capability of approximately 1.8 μm for fine particles. Under identical channel dimensions, a comparative analysis between conventional rectangular main channels and trapezoidal main channels was conducted, quantitatively showing that wall loss decreased from 44% to 24%. Based on the trapezoidal main channel configuration, further parametric studies on the horizontal inlet geometric dimensions were performed, revealing their influence on separation efficiency and wall loss. To validate the reliability of the numerical simulation results, particle separation experiments were conducted using polystyrene microspheres with particle sizes of 2 μm and 5 μm. Experimental results demonstrate that the virtual impactor can achieve stable particle separation and confirm the reliability of simulation-predicted particle classification trends. The results further show that, when driven by low-temperature CO2 combined with trapezoidal main channel structural optimization, the cut-off particle size of the virtual impactor decreases by approximately 26%, from 2.5 μm to about 1.8 μm. The trapezoidal channel structure significantly reduces particle wall loss under specific cut-off particle size conditions, while the low dynamic viscosity characteristic of low-temperature CO2 lowers the internal gas temperature environment of the microchannel, thereby improving inertial particle separation efficiency.

This study proposes a method for retrieving ocean sea surface salinity (SSS) using C/X-band ocean emissivities in coastal regions, aiming to verify the performance of these unconventional frequencies for SSS retrieval in warm, high-salinity-variation coastal oceans. Since C/X-band brightness temperatures are less sensitive to sea surface salinity than L-band brightness temperatures, it becomes particularly important to develop a sophisticated and effective method for extracting salinity-related signals from C/X-band brightness temperatures. To this end, a wind effect correction process is developed to remove rough sea surface emissivity contributions from total emissivity and derive calm sea emissivity from WindSat’s brightness temperatures. The wind-induced effects are modeled with a third-order polynomial. Then, based on emissivity analysis, a weighted combination of C/X-band calm sea emissivities (with parameter λ) is introduced to reduce SST sensitivity. This λ-based combination is used to retrieve SSS in the Bay of Bengal. Based on the triple-match method and buoy data, the salinity retrieval results are verified and compared with the Soil Moisture Active Passive (SMAP) SSS and Argo in situ SSS. The results show that the use of parameter λ reduces the RMS error of SSS by 0.1–0.2 psu. The RMSE of SSS retrieval is about 0.64 psu, which is comparable to the error of SMAP data. Simultaneously, the SSS retrieval accuracy is significantly influenced by offshore distance. At an offshore distance of 100 km, the salinity retrieval error exceeds 1 psu, while when the offshore distance exceeds 500 km, the salinity retrieval error is better than 0.6 psu.

Shallow-convective clouds (SCCs) play important roles in the Earth’s atmospheric system by affecting radiative balance, large-scale circulation, and transport of pollutants. It is common sense that topography exerts substantial impacts on SCCs. However, the underlying mechanisms are not well understood. Here, we performed large-eddy simulations (LESs) to investigate how three-dimensional (3D) topography affected SCCs. The 3D topography was constructed using two widely used two-dimensional (2D) topographies, a bell-shaped ridge varying in the x-direction and a series of sinusoidal ridges varying in the y-direction. The bell-shaped ridge was the major ridge. The upper parts and lower parts of the sinusoidal ridges were the minor ridges and minor valleys, respectively. The wavelength of the sinusoidal ridges was systematically varied. LESs were also performed separately using the 2D topographies. In the simulations with 3D topography, the upslope winds were mainly over the minor ridges and the return flows were mainly over the minor valleys, which was different from those in the simulations using 2D topographies. The upslope winds promoted the development of SCCs over the major ridges by producing large thermals and high humidity, similar to in the simulations using 2D topographies. Increasing the wavelength of minor ridges enlarged the region with convergence, and thereby increased the size of SCCs. Our results suggest that it is necessary to consider the 3D topography instead of the more conventional 2D topographies when investigating the topographic impacts on SCCs.

In order to improve the robustness and internal consistency of evapotranspiration estimation in arid regions and to reveal the characteristics of water consumption structure within a river basin, this study focused on the Aksu River Basin. Multiple data sources, including the Penman–Monteith model, MODIS remote sensing products, GRACE terrestrial water storage change data, and the GLDAS–Noah model, were integrated to establish a Bayesian Model Averaging (BMA)-based framework for fusing actual evapotranspiration (ETa) estimates. The results indicate that the BMA integration effectively mitigated model-dependent biases and improved the consistency and robustness of basin-scale ETa estimates. During the period 2000–2020, ETa in the basin exhibited an overall increasing trend (approximately 4.04 mm/a), with a spatial distribution pattern characterized by higher values in the northwest and lower values in the southeast. In terms of water consumption effectiveness, low-effectiveness water consumption predominated in the basin (accounting for 61.24%), while high-effectiveness water consumption accounted for a relatively smaller proportion (26.01%). These results suggest that the current water consumption structure remains dominated by low-effectiveness components, indicating potential room for optimization in balancing irrigation activities and ecosystem water use. The multi-source data fusion and water consumption effectiveness evaluation framework proposed in this study provides a scientific basis for water resource management and ecological water security assessment in arid river basins.

Against the backdrop of the ongoing advancement of China’s dual-carbon goals and the coordinated strategy for ecological protection and high-quality development in the Yellow River Basin (YRB), it is important to clarify the spatiotemporal dynamics of air pollution in the densely populated urban agglomerations of the mid–lower YRB. Using station-based daily observations from 2015 to 2024, this study examines six major air pollutants (PM2.5, PM10, CO, NO2, O3 and SO2) across the Shandong Peninsula, Central Plains, and Guanzhong Plain urban agglomerations. Sen’s slope estimator and the Mann–Kendall test are applied to quantify long-term trends, while partial correlation analysis and the GeoDetector model are used to diagnose pollutant co-variations and the drivers of spatial heterogeneity. Results indicate that while PM2.5, PM10, NO2, SO2, and CO concentrations significantly decreased, O3 exhibited a statistically significant upward trend (Z = 2.32, p = 0.02), particularly with pronounced summer maxima. PM2.5 shows clear seasonal variation, with elevated levels during winter and reduced levels during summer. Marked spatial contrasts are also observed: elevated particulate matter and CO are concentrated in the northern part of the Central Plains, while higher O3 levels are more evident in coastal areas, particularly within the Shandong Peninsula urban agglomeration. In terms of inter-pollutant relationships, particulate matter and CO are positively associated with SO2, whereas O3 is negatively correlated with NO2. GeoDetector results further suggest that air temperature, wind speed, and topography are the key factors associated with the spatial differentiation of pollutant levels; notably, the interaction between wind speed and temperature provides the greatest explanatory power, with effects that vary seasonally. These findings provide a scientific basis for region-specific air-pollution control and for advancing the co-benefits of carbon reduction and pollution mitigation in the YRB.

This study investigated an intense and unusual summer transboundary dust storm event that occurred between 21 and 23 June 2024. By integrating remote sensing observations, reanalysis data, WRF-Chem simulations, and LAGRANTO trajectory tracking, we systematically revealed the dust emission, transport, deposition, and formation mechanisms of this event. The dust primarily originated from the Gobi region of southern Mongolia, where concentrations exceeded 10,000 µg m−3, and decayed exponentially as the Mongolian cyclone moved southeastward. Post border-crossing into China, the event transitioned to blowing and floating dust, with concentrations decreasing significantly. During transport, dry deposition dominated the source area and the frontal part of the transport path in the early stages, while wet deposition was associated with the precipitation system of the Mongolian cyclone and concentrated north and east of the cyclone’s track. On 21 June 2024, the average wind speed in the source region reached 11.35 ms−1, the highest recorded in the past 45 years. This was attributed to surface anomalies, including reduced soil moisture, poor vegetation cover, higher temperatures, and decreased precipitation relative to the multi-year average. The comprehensive application of multi-source data and models in this work elucidates the full lifecycle of this rare summer dust event, providing scientific insights into the atmospheric processes governing extreme dust events and their transboundary impacts.