Search Results (50)

This study investigates the composition, abundance, and seasonal variability of airborne pollen in Siirt, a transitional region between the Irano-Turanian and Mediterranean phytogeographical zones in southeastern Türkiye. The main objective was to assess pollen diversity and its relationship with meteorological parameters over a two-year period (2022–2023). Airborne pollen was collected using a Hirst-type volumetric pollen and spore trap; a total of 18,666 pollen grains/m³ belonging to 37 taxa were identified. Of these, 70.67% originated from woody taxa and 29.33% from herbaceous taxa. Peak concentrations occurred in April, with the lowest levels in December. The dominant taxa, all exceeding 1% of the total, were Pinaceae (31.00%); Cupressaceae/Taxaceae (27.79%); Poaceae (18.42%); Moraceae (4.23%); Amaranthaceae (2.42%); Urticaceae (2.13%); Quercus (1.55%); Fabaceae (1.29%); and Rumex (1.02%). Spearman’s correlation analysis revealed significant relationships between daily pollen concentrations and meteorological variables such as temperature, humidity, precipitation, and wind speed. These findings highlight that both climatic conditions and the surrounding vegetation, shaped by regional land cover, play a crucial role in determining pollen dynamics. In conclusion, this study provides the first aerobiological baseline for Siirt and contributes valuable data for allergy-risk forecasting and long-term ecological monitoring in southeastern Türkiye. Full article

Methane hydrate is a crystalline compound in which methane is trapped within a water lattice under high-pressure, low-temperature conditions. Its presence in oceanic and permafrost sediments makes it a promising alternative energy source, but also a potential contributor to climate change. Accurate in situ detection remains a major challenge due to hydrate’s dispersed occurrence and the limitations of conventional geophysical methods. This study investigates the feasibility of using the associated alpha particle (AAP) technique for the direct detection of methane hydrate. A series of laboratory measurements was conducted on sand-based samples with varying levels of methane hydrate simulant. Using a 14 MeV neutron generator and a LaBr₃ gamma detector, the 4.44 MeV carbon peak was monitored and calibrated against volumetric hydrate saturation. The minimum detection limit (MDL) was experimentally determined to be $(67\pm 25)\%$. Although the result is subject to high uncertainty, it provides a preliminary benchmark for evaluating the method’s sensitivity and highlights the potential of AAP-based gamma spectroscopy for in situ detection, especially when supported by higher neutron flux in future applications. Full article

Background: There is a lack of automatic real-time monitoring of airborne pollens in China and no validation study has been performed. Methods: Two-year continuous automatic real-time pollen monitoring (n = 437) was completed in 2023 (3 April–31 December) and 2024 (1 April–30 November) in Shanghai, China, in parallel with the standard daily pollen sampling(n = 437) using a volumetric Hirst sampler (Hirst-type trap, according to the European standard). Daily ambient particulate matter and meteorological factors were collected simultaneously. Results: Across 2023 and 2024, the daily mean pollen concentration was 7 ± 9 (mean ± standard deviation (SD)) grains/m³ by automatic monitoring and 8 ± 10 grains/m³ by the standard Hirst-type method, respectively. The spring season had higher daily pollen levels by both methods (11 ± 14 grains/m³ and 12 ± 15 grains/m³) and the daily maximum reached 106 grains/m³ and 100 grains/m³, respectively. A strong correlation was observed between the two methods by either Pearson (coefficient 0.87, p < 0.001) or Spearman’s rank correlation (coefficient 0.70, p < 0.001). Compared to the standard method, both simple (R² = 0.76) and multiple linear regression models (R₂ = 0.76) showed a relatively high goodness of fit, which remained robust using a 5-fold cross-validation approach. The multiple regression mode adjusted for five additional covariates: daily mean temperature, relative humidity, wind speed, precipitation, and PM₁₀. In the subset of samples with daily pollen concentration ≥ 10 grains/m³ (n = 98) and in the spring season (n = 145), the simple linear models remained robust and performed even better (R² = 0.71 and 0.83). Conclusions: This is the first validation study on automatic real-time pollen monitoring by volumetric concentrations in China against the international standard manual method. A reliable and feasible simple linear regression model was determined to be adequate, and days with higher pollen levels (≥10 grains/m³) and in the spring season showed better fitness. More validation studies are needed in places with different ecological and climate characteristics to promote the volumetric real-time monitoring of pollens in China. Full article

The increasing global energy demand, coupled with the urgent need to reduce CO₂ emissions, has intensified the search for renewable energy sources. Biogas, produced from agro-industrial biomass, presents a viable solution. In beer production, brewery’s spent grain (BSG), the largest by-product by volume, offers potential for bioenergy recovery. This study applied a biorefinery approach to BSG, extracting protein hydrolysates (PH) through mild alkaline hydrolysis and nanostructured lignin (LN) via the Ionic Liquid Method. The objective was to assess biogas production from the residual biorefinery biomass and evaluate the co-digestion of BSG with Olive Mill Wastewater (OMWW) and Olive Pomace (OP), by-products of the olive oil industry. Biogas was produced in lab-scale batch reactors and the quantity of biogas produced was measured via the volumetric method. Conversely, the amount of biomethane obtained was evaluated by introducing, in the production chain, an alkaline trap. Biogas yields were the highest for untreated BSG (1075.6 mL), co-digested BSG with OMWW (1130.1 mL), and BSG residue after PH extraction (814.9 mL). The concentration of biomethane obtained in the various samples ranged from 54.5 vol % (OMWW + BSG) to 76.59 vol % (BSG). An energy balance analysis considering both the theoretical energy consumed by a semi-continuous anaerobic digestion bioreactor and the energy produced as bio-CH₄ revealed that BSG after PH extraction was the most energy-efficient treatment, producing a net energy gain of 5.36 kJ. For the scope, the energy consumption was calculated by considering a PEIO index equal to 33% of the energy produced during the day, showing the highest biogas production. In contrast, the co-digested BSG with OMWW yielded the lowest net energy gain of 1.96 kJ. This comprehensive analysis highlights the energy efficiency of different treatments, identifying which process should be improved. Full article

Low ventilation rates used to conserve energy in pig nurseries in winter can worsen air quality, harming piglet health. A recirculating air cleaner consisting of a dust filter and ultraviolet C (UVC) lamps was evaluated in a pig nursery. It had a recirculation rate of 6.4 air changes per hour, residence time of 0.43 s, and UVC volumetric dose of 150 J·m⁻³. Reduced ventilation led to high particulate matter (PM) concentrations in the nursery. During the first 9 d, the air cleaner increased floor temperature in its vicinity by 1.9 °C vs. a more distant location. The air cleaner had average removal efficiencies of 29 and 27% for PM_2.5 (PM with aerodynamic equivalent diameter or AED < 2.5 µm) and PM₁₀ (PM with AED < 10 µm), respectively. It reduced PM_2.5 and PM₁₀ concentrations by 38 and 39%, respectively, in its vicinity vs. a more distant location. The air cleaner was mostly inconsistent in inactivating heterotrophic bacteria, but it eliminated fungi. It trapped 56% of the ammonia but did not trap nitrous oxide, methane, or carbon dioxide. The air cleaner demonstrated the potential for reducing butanoic, propanoic, and pentanoic acids. Design improvements using modeling and further testing are required. Full article

Binary As_xSe_100−x alloys from the border of a glass-forming region (65 < x < 70) subjected to nanomilling in dry and dry–wet modes are characterized by the XRPD, micro-Raman scattering (micro-RS) and revised positron annihilation lifetime (PAL) methods complemented by a disproportionality analysis using the quantum–chemical cluster modeling approach. These alloys are examined with respect to tetra-arsenic biselenide As₄Se₂ stoichiometry, realized in glassy g-As₆₅Se₃₅, glassy–crystalline g/c-As₆₇Se₃₃ and glassy–crystalline g/c-As₇₀Se₃₀. From the XRPD results, the number of rhombohedral As and cubic arsenolite As₂O₃ phases in As-Se alloys increases after nanomilling, especially in the wet mode realized in a PVP water solution. Nanomilling-driven amorphization and reamorphization transformations in these alloys are identified by an analysis of diffuse peak halos in their XRPD patterning, showing the interplay between the levels of a medium-range structure (disruption of the intermediate-range ordering at the cost of an extended-range one). From the micro-RS spectroscopy results, these alloys are stabilized by molecular thioarsenides As₄Se_n (n = 3, 4), regardless of their phase composition, remnants of thioarsenide molecules destructed under nanomilling being reincorporated into a glass network undergoing a polyamorphic transition. From the PAL spectroscopy results, volumetric changes in the wet-milled alloys with respect to the dry-milled ones are identified as resulting from a direct conversion of the bound positron–electron (Ps, positronium) states in the positron traps. Ps-hosting holes in the PVP medium appear instead of positron traps, with ~0.36–0.38 ns lifetimes ascribed to multivacancies in the As-Se matrix. The superposition of PAL spectrum peaks and tails for pelletized PVP, unmilled, dry-milled, and dry–wet-milled As-Se samples shows a spectacular smoothly decaying trend. The microstructure scenarios of the spontaneous (under quenching) and activated (under nanomilling) decomposition of principal network clusters in As₄Se₂-bearing arsenoselenides are recognized. Over-constrained As_6·(2/3) ring-like network clusters acting as pre-cursors of the rhombohedral As phase are the main products of this decomposition. Two spontaneous processes for creating thioarsenides with crystalline counterparts explain the location of the glass-forming border in an As-Se system near the As₄Se₂ composition, while an activated decomposition process for creating layered As₂Se₃ structures is responsible for the nanomilling-driven molecular-to-network transition. Full article

The olive tree is one of the most common type of cultivation in the Mediterranean area, having high economic and social importance. The Alentejo region, Portugal, is an area with a high presence of olive groves, which in 2022 accounted for 201,474 hectares. The aim of this study was to assess the relationship between olive pollen, weather data, and olive tree production, between the years 2002 and 2022. Pollen data were obtained from an urban station located in Évora, in the Alentejo region, and were used to calculate several metrics, such as the Pollen Season Duration (PSD), Seasonal Pollen Index (SPIn), peak value, and weekly pollen accumulation values. Monthly minimum, maximum, and mean temperature and precipitation sums were obtained from the E-OBS observational dataset. Considering the relationship between pollen/weather and olive production, mutual information and correlation analyses were conducted. Subsequently, several machine learning algorithms were trained using pollen and weather datasets, and we obtained suitable forecast models for olive tree production after cross-validation. The results showed high variability in pollen concentrations in Évora over the years. Complex associations were found, with certain weeks of pollen accumulation showing significant mutual information with olive production, particularly during June. The analyzed linear correlation coefficients remained generally low, underscoring the challenge of predicting olive production based on linear relationships. Among the machine learning algorithms employed to predict olive production, Decision Trees, Extreme Gradient Boosting, and Gradient Boosting Regressor were the most robust performers (r² > 0.70), while linear models displayed a subpar performance (r² < 0.5), emphasizing the complexity of this approach. These models highlight the roles of maximum and minimum temperatures during March and May and pollen accumulation during the second half of June. The developed models may be used as decision-support tools by growers and stakeholders to further enhance the sustainability of the thriving olive sector in southern Portugal. Full article

Coastal communities commonly rely upon foredunes as the first line of defense against sea-level rise and storms, thus requiring management guidance to optimize their protective services. Here, we use the AeoLiS model to simulate wind-driven accretion and wave-driven erosion patterns on foredunes with different morphologies and ecological properties under modern-day conditions. Additional sets of model runs mimic potential future climate changes to inform how both morphological and ecological properties may have differing contributions to net dune changes under evolving environmental forcing. This exploratory study, applied to represent the morphological, environmental, and ecological conditions of the northern Outer Banks, North Carolina, USA, finds that dunes experiencing minimal wave collision have similar net volumetric growth rates regardless of beach morphology, though the location and density of vegetation influence sediment deposition patterns across the dune profile. The model indicates that high-density, uniform planting strategies trap sediment close to the dune toe, whereas low-density plantings may allow for accretion across a broader extent of the dune face. The initial beach and dune shape generally plays a larger role in annual-scale dune evolution than vegetation cover. For steeper beach slopes and/or low dune toe elevations, the model generally predicts wave-driven dune erosion at the annual scale. Full article

Traditionally, single-color laser beams are used for material processing and modifications of optical, mechanical, conductive, and thermal properties of different materials. So far, there are a limited number of studies about the dual-wavelength laser irradiation of materials, which, however, indicate a strong enhancement in laser energy coupling to solid targets. Here, a theoretical study is reported that aimed at exploring the volumetric excitation of fused silica with dual-wavelength (800 nm and 400 nm) ultrashort laser pulses focused on the material’s bulk. Numerical simulations are based on Maxwell’s equations, accounting for the generation of conduction electrons, their hydrodynamic motion in the laser field, and trapping into an excitonic state. It is shown that, by properly choosing the energies of the two laser harmonics successively coupling with the material, it is possible to strongly enhance the laser energy absorption as compared to the pulses of a single wavelength with the same total energy. Laser energy absorption strongly depends on the sequence of applied wavelengths, so that the shorter wavelength pre-irradiation can yield a dramatic effect on laser excitation by the following longer-wavelength pulse. The predictions of this study can open a new route for enhancing and controlling the highly localized absorption of laser energy inside transparent materials for optoelectronic and photonic applications. Full article

A computational model that can accurately describe the thermodynamics of a hydrocarbon system and its properties under various conditions is a prerequisite for running reservoir and pipeline simulations. Cubic Equations of State (EoS) are mathematical tools used to model the phase and volumetric behavior of reservoir fluids when compositional effects need to be considered. To anticipate uncertainty and enhance the quality of their predictions, EoS models must be adjusted to adequately match the available lab-measured PVT values. This task is challenging given that there are many potential tuning parameters, thus leading to various tuning results of questionable validity. In this paper, we present an automated EoS tuning workflow that employs a Generalized Pattern Search (GPS) optimizer for efficient tuning of a cubic EoS model. Specifically, we focus on the Peng–Robinson (PR) model, which is the oil and gas industry standard, to accurately capture the behavior of diverse multicomponent, complex hydrocarbon mixtures encountered in subsurface reservoirs. This approach surpasses the limitations of conventional gradient-based (GB) methods, which are susceptible to getting trapped in local optima. The proposed technique also allows physical constraints to be imposed on the optimization procedure. A gas condensate and an H₂S-rich oil were used to demonstrate the effectiveness of the GPS algorithm in finding an optimized solution for high-dimensional search spaces, and its superiority over conventional gradient-based optimization was confirmed by automatically tracking globally optimal and physically sound solutions. Full article

Our study aimed to quantify the pollen trapped by the seeds and relate it with the airborne pollen concentrations. Individual Populus spp. tuft-like seeds were sampled while suspended twice a day in 2017 and 2018 during the seed dispersal season. The seeds were submitted to laboratory treatment for pollen extraction, which was quantified using an optical microscope. Airborne pollen was monitored using a seven-day Hirst-type volumetric spore sampler. A correlation analysis was performed between the airborne pollen, the pollen on the seeds, and the meteorological parameters. A total of 26 pollen grains/mg was counted in the airborne tuft-like seeds, with 26 different taxa being identified, compared with the 18 pollen taxa identified in the airborne samples. Quercus, Poaceae, Urticaceae, Pinus, and Platanus were the most frequent pollen found on the seeds, while in the atmosphere, pollen from Urticaceae, Quercus, and Cupressaceae were the most representative. A tendency of higher pollen concentrations found in the afternoon samples, both airborne and on the seeds, was observed. Correlations between the meteorological parameters and pollen concentration found airborne and in the seeds were overall not significant. Thus, airborne poplar tuft-like seeds can trap and transport pollen, most of which has been recognized to induce respiratory allergies. Full article

Conservation alternatives that include no-tillage (NT) and cover crops (CCs) reduce soil erosion in row-crop agroecosystems. However, little information is available about how these alternatives affect soil textural properties responsible for soil fertility. This study evaluated the soil particle size distribution and volumetric water content after three years of consistent management in a raised bed system. There were four treatment systems in a dryland maize/soybean rotation on a silt loam soil (Oxyaquic Fraglossudalfs) that included: NT + CCs, conventional tillage (CT) + CCs, CT + winter weeds, and CT + bare soil in winter in northwest Mississippi. The NT + CC system retained 62% more coarse sand in the furrow than the other systems (2.1% compared to 1.3%; p = 0.02). Regardless of the location, the NT + CC system (2.5%) retained 39% more fine sand than the CT + CC system (1.8%; p = 0.01), suggesting that coarse and fine sands were being trapped in furrows combining NT + CC systems, minimizing their off-site transport. In furrows, CCs increased soil volumetric water content by 47% compared to other winter covers. In beds, NT + CCs increased bed water contents by 20% compared to CT + CCs (17.1 to 14.3%; p < 0.01). Implementing conservation alternatives may promote the retention of sand fractions in silty loam soils that are important in supporting soil fertility and crop sustainability. Full article

Room-temperature semiconductor radiation detectors (RTSD) such as CdZnTe are popular in Computed Tomography (CT) imaging and other applications. Transport properties and material defects with respect to electron and hole transport often need to be characterized, which is a labor intensive process. However, these defects often vary from one RTSD to another and are not known a priori during characterization of the material. In recent years, physics-inspired machine learning (PI-ML) models have been developed for the RTSDs which have the ability to characterize the defects in a RTSD by discretizing it volumetrically. These learning models capture the heterogeneity of the defects in the RTSD—which arises due to the fabrication process and the energy bands of elements in the RTSD. In those models, the different defects of RTSD—trapping, detrapping and recombination for electrons and holes—are present. However, these defects are often unknown. In this work, we show the capabilities of a PI-ML model which has been developed considering all the material defects to identify certain defects which are present (or absent). Additionally, these models can identify the defects over the volume of the RTSD in a discretized manner. Full article

Nutrient recycling is one of the most important services that supports other processes in ecosystems. Changing litterfall patterns induced by climate change can cause imbalances in nutrient availability. In this study, we reported the long-term (28-year) interplay between environmental factors and variability among litterfall fractions (leaves, flowers, and fruit) in a tropical dry forest located in Kanchanaburi, Thailand. A long-term litter trap dataset was collected and analyzed by lagged generalized additive models. Strong seasonality was observed among the litter fractions. The greatest leaf and flower litterfall accumulated mostly during the cool, dry season, while fruit litterfall occurred mostly during the rainy season. For leaf litter, significant deviations in maximum temperature (Tmax), volumetric soil moisture content (SM), and evapotranspiration (ET) during the months prior to the current litterfall month were the most plausible factors affecting leaf litter production. Vapor pressure deficit (VPD) and ET were isolated as the most significant factors affecting flower litterfall. Interestingly, light, mean temperature (Tmean), and the southern oscillation index (SOI) were the most significant factors affecting fruit litterfall, and wetter years proved to be highly correlated with elevated fruit litterfall. Such environmental variability affects both the triggering of litterfall and its quantity. Shifting environmental conditions can therefore alter nutrient recycling rates through the changing characteristics and quantity of litter. Full article

Silicon is a desirable anode material for Li-ion batteries owing to its remarkable theoretical specific capacity of over 4000 mAh/g. Nevertheless, the poor cycling performance of pure Si electrodes caused by dramatic volume expansion has limited its practical application. To alleviate the adverse effects of Si expansion, we have synthesized anode materials of nano-Si particles trapped in a buffering space and outer carbon-based shells (Si@Void@C). The volume ratio of Si nanoparticle to void space could be adjusted accurately to approximately 1:3, which maintained the structural integrity of the as-designed nanoarchitecture during lithiation/delithiation and achieved a notable specific capacity of ~750 mAh/g for as-prepared half-cells. The yolk-shell nanostructure alleviates volumetric expansion on both material and electrode levels, which enhances the rate performance and cycling stability of the silicon-based anode. Full article