Search Results (5,355)

Hydroclimatic and geomorphological prerequisites for mudflow hazard were studied using data on several of the largest flood events in the Khamar-Daban mountain area (Lake Baikal, East Siberia) for the period from 1966 to 2022. The data include flood-forming precipitation and atmospheric circulation patterns, the amount of related suspended sediment discharge in the years of high floods, as well as terrain features favorable for the formation of catastrophic floods and mudflows. Floods and mudflows in the area can arise under conditions of extremely high daily precipitation (up to 200 mm or more) after the territory becomes moistened by prolonged rainfall under meridional air transport. The maximum water discharge correlates with a multifold increase in the suspended sediment discharge and turbidity. The increase in sediment discharge associated with maximum water discharge (floods) of ≤10% probability is apparently due to 4–9 times higher flow rates. On the other hand, the formation of the solid runoff component in the area is controlled geomorphologically by slope processes depending on slope steepness, elevation contrasts, and the thickness of soft sediments subject to denudation and transport. The geomorphological conditions are most favorable for the development of mudflows and catastrophic floods in the catchments of the Bezymyannaya, Slyudyanka, Khara-Murin, and Utulik rivers. Floods and mudflows are especially hazardous on the southern shore of Lake Baikal, encircled by the Khamar-Daban Range, where active mudflow processes pose risks to the towns of Slyudyanka and Baikalsk, as well as to the sludge storage facilities of the abandoned Baikal Pulp and Paper Mill. Full article

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

The biogeochemical cycling of nitrogen (N) in natural waterbodies, ranging from freshwaters to estuaries and seawater, is fundamental to the health of aquatic ecosystems. Anthropogenic pressures (agricultural runoff, atmospheric deposition, and wastewater discharge) have profound effects on these cycles, leading to widespread problems, such as eutrophication, harmful algal blooms, and contamination of drinking water sources. Monitoring of different N-species—ammonium (NH₄⁺), nitrite (NO₂⁻), nitrate (NO₃⁻) ions, dissolved organic nitrogen (DON), and total nitrogen (TN)—is of crucial importance to protect and mitigate environmental harm. Traditional analytical methodologies, while providing accurate laboratory data, are hampered by logistical complexity, high cost, and the inability to capture transient environmental events in near-real time. In response to this demand, miniaturised microfluidic technologies offer the opportunity for rapid, on-site measurements with significantly reduced reagent/sample consumption and the development of portable sensors. Here, we review and critically evaluate the principles, state-of-the-art applications, inherent advantages, and ongoing challenges associated with the use of microfluidic colorimetry for N-species in a variety of environmental waterbodies. We explore adaptations of classical colorimetric chemistry to microfluidic-based formats, examine strategies to mitigate complex matrix interferences, and consider future trajectories with autonomous platforms and smart sensor networks for simultaneous multiplexed N-species determination. Full article

Understanding long-term cloud cover variability is essential for assessing past climate dynamics and human influences on atmospheric conditions. In Padua, instrumental weather records (temperature, precipitation, pressure) and descriptive sky observations date back to 1725, but quantitative cloud cover data, expressed as tenths of the sky covered by clouds, began in 1872 at the Astronomical Observatory. From 1920 to 1989, observations continued under the authority of the Meteorological Observatory of the Water Magistrate, and from 1951 to 1990, additional records by the Italian Air Force expressed in eighths of sky are available. These visual datasets—based on multiple daily observations—are complemented by satellite records (from 1983) and reanalysis such as ERA5 (from 1940) and NOAA 20CRv3 (from 1872 to 2015). The aim of this study is to reconstruct a homogenized, long-term total cloud cover (TCC) time series for Padua from 1872 to 2024, integrating all available observational sources. By comparing overlapping periods across different subseries and nearby ground-based stations, the analysis not only investigates consistency and potential discontinuities across datasets but also quantifies the reliability and limitations of historical visual observations. This work provides one of the few centennial-scale reconstructions of cloud cover in Europe, offering a valuable contribution to historical climatology and climate change studies. Full article

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

Owing to their high vertical resolution, remote sensing data from meteorological satellite hyperspectral infrared sounders are well-suited for the identification, monitoring, and early warning of high-impact weather events. The effective utilization of full field-of-view (FOV) observations from satellite infrared sounders in high-impact weather applications remains a major research focus and technical challenge worldwide. This study proposes a generalized variational retrieval framework to estimate full FOV cloud fraction and precipitable water vapor (PWV) from observations of the Geostationary Interferometric Infrared Sounder (GIIRS) onboard the Fengyun-4A (FY-4A) satellite. Based on this method, experiments are performed using high-frequency FY-4A/GIIRS observations during the landfall periods of Typhoon Lekima (2019) and Typhoon Higos (2020). A three-step channel selection strategy based on information entropy is first designed for FY-4A/GIIRS. A constrained generalized variational retrieval method coupled with a cloud cost function is then established. Cloud parameters, including effective cloud fraction and cloud-top pressure, are initially retrieved using the Minimum Residual Method (MRM) and used as initial cloud information. These parameters are iteratively optimized through cost-function minimization, yielding full FOV cloud fields and atmospheric profiles. Full FOV brightness temperature simulations are conducted over cloudy regions to quantitatively evaluate the retrieved cloud fractions, and the derived PWV is further applied to the identification and analysis of hazardous weather events. Experimental results demonstrate that incorporating cloud parameters as auxiliary inputs to the radiative transfer model improves the simulation of FY-4A/GIIRS brightness temperature in cloud-covered areas and reduces brightness temperature biases. Compared with ERA5 Total Column Water Vapour (TCWV) data, the PWV derived from full FOV profiles containing cloud parameter information shows closer agreement and, at certain FOVs, more effectively indicates the occurrence of high-impact weather events. The simplified methodology proposed in this study provides a robust basis for the future assimilation and operational utilization of infrared data over cloud-affected regions in numerical weather prediction models. Full article

With the development of global maritime trade, ship emissions pose an increasing threat to the global atmospheric environment, especially in international navigation waters and important port areas, where their impact on coastal air quality and ecosystems is becoming increasingly significant. This study proposes a high-throughput gridding algorithm (H-Grid) based on spatial hashing to rapidly generate ship emission inventories, which overcomes the inefficiency of traditional methods caused by complex index building and maintenance. The H-Grid algorithm achieves a constant processing time per data point and possesses inherent parallelism. Based on the H-Grid algorithm, taking the Yellow Sea area between China and Republic of Korea as a case study, the emissions of atmospheric pollutants from ships in 2024 were calculated, and their spatiotemporal distribution characteristics were analyzed. In our empirical study, the algorithm’s computational efficiency for processing millions of AIS records was improved by over 10 times compared to traditional geometric calculations, and by more than 4 times when compared to mainstream database spatial queries. Our findings provide an efficient tool for large-scale maritime emission analysis, strongly supporting the green development of global shipping. Full article

Excessive nitrogen addition in farmland on the Loess Plateau reduces soil quality and endangers the atmospheric environment. We designed an experiment to investigate the effects of different nitrogen application rates on the soil physicochemical properties and microbial diversity of spring wheat fields on the Loess Plateau, aiming to identify the optimal nitrogen application rate and avoid the detrimental effects of excessive nitrogen addition. A field experiment was conducted from 2022 to 2023 with four nitrogen (N) application rates (0, 55, 110, and 220 kg·N·ha⁻¹·y⁻¹). This study aimed to assess the changes in soil properties, nutrient contents, enzyme activities, and bacterial community structure. The results showed that increasing N application generally enhanced soil bulk density, nitrate nitrogen (NO₃⁻-N), ammonium nitrogen (NH₄⁺-N), and microbial biomass nitrogen (MBN) (p < 0.05). In contrast, soil water content initially increased and then decreased. Soil organic carbon and total nitrogen rose markedly with higher N inputs, particularly in the 0–20 cm layer, whereas total phosphorus was less affected. Nitrogen addition stimulated soil enzyme activities (protease, urease, nitrate reductase, and nitrite reductase), though excessive input (220 kg·N·ha⁻¹·y⁻¹) produced inhibitory effects. Actinobacteria (relative abundance: 29–35%) and Proteobacteria (relative abundance: 14–22%) were the dominant phyla in all treatments. Alpha diversity peaked at low nitrogen input (55 kg·N·ha⁻¹·y⁻¹), while high N level reduced evenness and species richness (p < 0.05). Principle Coordinate Analysis (PCoA) revealed that both N application and soil depth shaped microbial community assembly, with deeper layers (20–40 cm) being more sensitive to N input. Correlation analysis indicated that soil moisture, bulk density, and C:N:P stoichiometry were key drivers of bacterial community variation. Overall, moderate nitrogen input (110 kg·N·ha⁻¹·y⁻¹) improved soil fertility and supported microbial functionality, whereas excessive application degraded soil structure and reduced biodiversity. These findings highlight the need for balanced N management strategies in rain-fed agriculture of the Loess Plateau to sustain both productivity and ecological stability. Full article

Pluriannual time series of fine particle concentrations suspended in the atmosphere are often lacking. Such data is necessary in evaluating the efficiency of policies aiming to improve air quality in megacities. In this work, a recently developed empirical method is applied over the megacities of Paris, Cairo, and New Delhi. The method utilizes observations of the aerosol optical depth, Angström Exponent, and atmospheric precipitable water as inputs to estimate the PM10. The modeled values validated against their respective reference measurements exhibited the best performance at daily, weekly, and monthly averages when using inputs of the AERONET. When exploiting inputs of the CAMS and MERRA-2 reanalyses, the results were found to be satisfactory with MERRA-2 on the monthly scale. This allows the reconstruction of the variability of the PM10 for the last 45 years. Analysis shows that average annual PM10 concentration has decreased from 40 to 20 µg·m⁻³ in Paris, increased from 70 to 250 µg·m⁻³ in New Delhi, and stayed relatively stable (around 100 µg·m⁻³) in Cairo. Provided that at least one year of PM10 measurements are available to calibrate the empirical method, the method herein is replicable over other megacities around the world. Full article

Soil water availability and nitrogen (N) deposition critically influence biogenic volatile organic compound (BVOC) emissions, thereby affecting atmospheric chemistry. However, their differential short- and long-term effects remain unclear. Here, Ormosia pinnata and Pinus massoniana seedlings were exposed to three water regimes (moderate drought, MD; normal irrigation, NI; near-saturated irrigation, NSI) and two nitrogen (N0; 0 kg N ha⁻¹ yr⁻¹; N80; 80 kg N ha⁻¹ yr⁻¹) treatments for 20 months. Branch-level BVOC emissions and leaf physiological and biochemical traits were examined after 8 months (short term) and 16 months (long term). In the short term, P. massoniana predominantly emitted α-pinene, β-pinene, and γ-terpinene, whereas O. pinnata emitted isoprene (ISO). After prolonged exposure, ISO became the dominant in both species. Short-term MD and NSI conditions stimulated ISO emissions in O. pinnata, with N80 addition further amplifying this effect. In contrast, long-term treatments tended to suppress ISO emissions in O. pinnata, particularly under N80. Short-term water treatments had no significant effect on monoterpene (MT) emissions in P. massoniana. Under long-term water treatments, N80 suppressed ISO emissions; nevertheless, ISO emission rates (ISOrate) progressively increased with increasing soil water availability. Although leaf intercellular CO₂ concentration (Ci), stomatal conductance (g_s), and photosynthesis-related enzymes exhibited partial correlations with BVOC emissions, an overall decoupling between leaf traits and emission patterns was evident. Our findings demonstrate the significant changes in both BVOC composition and emission magnitudes under the joint effects of water availability and nitrogen deposition, providing important implications for improving regional air quality modeling and BVOC emission predictions. Full article

Globally, little is known about the dispersal of microplastics (MP) and anthropogenic particles (AP) via atmospheric deposition (AD) into water bodies. Correlating AD to the large number of MP in estuaries is challenging but an important first step toward reducing this form of pollution. A previously published model of the surface waters of the Indian River Lagoon (IRL, east central coast of Florida, USA) estimated it contained 1.4 trillion microplastics. To determine if AD could produce this much plastic deposition, we deployed passive AD collectors throughout a 145 km² area at three site types with assistance from citizen scientists. We predicted that the rate of deposition of MP and AP would be greatest in residential areas, intermediate within a national park, and lowest on intertidal oyster reefs. Moreover, we predicted Florida’s wet season and individual rain events would increase deposition based on the published literature. Over 14 months, deposition averaged 1224 MP/m²/d; extrapolated, this yields 1.1 trillion MP for the lagoon-wide total deposition estimate (95% CI: 0.86–1.39 trillion MP). This value suggests that AD may represent an important pathway for MP to enter this estuary. More MP were deposited during rain events and in the wet season, with no differences among sites. Overall, our results provide important data for understanding AD of MP and AP in estuaries. Full article

The study of the relationship between water availability, photosynthetic behavior, flavonoid accumulation, and antioxidant response offers new perspectives for enhancing nursery practices, resulting in more vigorous eucalyptus seedlings that are tolerant and have greater potential for field establishment. Under the hypothesis that different eucalyptus genetic materials show contrasting responses to water availability in the soil–plant–atmosphere system, this study aims to evaluate the physiological behavior of clones subjected to different irrigation intervals, with an emphasis on the role of flavonoids as antioxidants in mitigating the effects of water stress. The experimental design was structured in strips containing five eucalyptus clones and irrigation with different watering intervals: 1, 2, 4, and 8 days. Evaluations of net photosynthesis, transpiration, and instantaneous water use efficiency were performed. In addition to the physiological assessments, the flavonoids daidzein, genistein, and genistin were determined. Clones C1, C2, and C3 excelled in photosynthesis and transpiration at 2- and 4-day intervals, while C1 and C2 maintained superior performance even at an 8-day interval. WUE was highest in C5 and increased with water stress, showing a quadratic fit in all clones. Regarding flavonoid production, C1 and C3 showed greater daidzein accumulation, with a quadratic response to the withdrawal interval. Genistein showed a linear reduction only in C2, while genistein increased in C1, peaking around 11 days. Eucalyptus clones exhibit distinct physiological and biochemical responses to variations in irrigation intervals. More frequent irrigation favors photosynthetic activity and transpiration, particularly in clones C1, C2, and C3, whereas longer irrigation intervals reduce these processes but enhance water use efficiency, especially in C5. Full article

Aluminum nitride (AlN) is a wide-bandgap semiconductor (6.2 eV) with a broad transparency window spanning from the ultraviolet (UV) to the mid-infrared (MIR) wavelength region, making it a promising material for integrated photonics. In this work, AlN thin films using reactive RF sputtering are deposited, followed by annealing at 600 °C in a nitrogen atmosphere to reduce slab waveguide propagation losses. After annealing, the measured loss is 0.84 dB/cm at 978 nm, determined using the prism coupling method. A complete microfabrication process flow is then developed for the realization of optical channel waveguides. A key challenge in the processing of AlN is its susceptibility to oxidation when exposed to water or oxygen plasma, which significantly impacts device performance. The process is validated through the fabrication of microring resonators (MRRs), used to characterize the propagation losses of the AlN channel waveguides. The fabricated MRRs exhibit a quality factor of 12,000, corresponding to a propagation loss of 4.4 dB/cm at 1510–1515 nm. The dominant loss mechanisms are identified, and strategies for further process optimization are proposed. Full article

Jiangnan water towns, as distinctive cultural landscapes in China, are confronting the dual challenge of surging tourist flows and imbalances in spatial distribution. Research on pedestrian dynamics has so far offered narrow coverage of influencing factors and limited insight into underlying mechanisms, falling short of a systemic perspective and an interpretable theoretical framework. This study uses Nanxun Ancient Town as a case study to address this gap. Pedestrian trajectories were captured using temporarily installed closed-circuit television (CCTV) cameras within the scenic area and extracted using the YOLOv8 object detection algorithm. These data were then integrated with quantified environmental indicators and analyzed through Random Forest regression with SHapley Additive exPlanations (SHAP) interpretation, enabling quantitative and interpretable exploration of pedestrian dynamics. The results indicate nonlinear and context-dependent effects of environmental factors on pedestrian dynamics and that tourist flows are jointly shaped by multi-level, multi-type factors and their interrelations, producing complex and adaptive impact pathways. First, within this enclosed scenic area, spatial morphology—such as lane width, ground height, and walking distance to entrances—imposes fundamental constraints on global crowd distributions and movement patterns, whereas spatial accessibility does not display its usual salience in this context. Second, perceptual and functional attributes—including visual attractiveness, shading, and commercial points of interest—cultivate local “visiting atmospheres” through place imagery, perceived comfort, and commercial activity. Finally, nodal elements—such as signboards, temporary vendors, and public service facilities—produce multi-scale, site-centered effects that anchor and perturb flows and reinforce lingering, backtracking, and clustering at bridgeheads, squares, and comparable nodes. This study advances a shift from static and global description to a mechanism-oriented explanatory framework and clarifies the differentiated roles and linkages among environmental factors by integrating video-based trajectory analytics with machine learning interpretation. This framework demonstrates the applicability of surveillance and computer vision techniques for studying pedestrian dynamics in small-scale heritage settings, and offers practical guidance for heritage conservation and sustainable tourism management in similar historic environments. Full article

The widespread and excessive use of plastic in our daily life has led to serious microplastic pollution in the atmosphere, water, and soil. These microplastics can enter freshwater systems and pose significant risks to the ecosystem and human health via the food chain. This environmental problem deserves proper investigation and mitigation strategies. In this study, the abundance, morphology, color, size and polymer composition of microplastics in surface water of Feiyun River Basin were systematically studied by means of field sampling, microscopy and laser micro-Raman spectroscopy. The result showed that microplastic abundance ranged from 3.7 to 36.4 items/L, with an average of 11.0 ± 2.39 items/L. These microplastics were mainly particles, followed by fragments and fibers, with white, black, and blue being the most common colors. Most of the particles were smaller than 0.1 mm (57%), and a laser micro-Raman spectrometer was used to identify the polymer types of the microplastics. The results showed that the main polymer types identified were PET, PP, and PS. Risk assessment based on PLI, PHI, and PERI indices indicated a low ecological risk of microplastics in the study area. These findings provide further insight into the sources and distribution of microplastics in local watersheds and support future assessments of riverine transport of microplastics to estuarine and marine environments. Full article