Search Results (2,668)

The technique of Dynamic Factor Analysis (DFA), which aims to reduce the dimensionality of time-series data, was utilized in order to model the changes over time in eight different long-time-series weeds (26 years) growing in a biennial cereal–legume rotation. The aim of the present study was to determine the existence of long-term trends in a weed community and to identify the factors that determine them. A common trend was extracted that captured the main signal of abundance over time, indicating latent influences affecting all species. Canonical correlation analysis showed strong associations between the common trend and specific weed species, suggesting differential responses to this latent factor. Local (temperature and precipitation) and global weather factors (North Atlantic Oscillation (NAO)) were considered as explanatory variables to explain the common trend. The local weather variables considered did not play a significant role in explaining the commonly observed trend. Conversely, NAO showed a significant relationship with the weed community, indicating its potential role in shaping long-term weed dynamics. DFA was found to be useful for studying the variability in multivariate weed time-series without the need for detailed a priori information on the underlying mechanisms governing weed population dynamics. Overall, this study provided valuable insights into the long-term drivers of weed dynamics and set the stage for future research in this area. Full article

This study provides a comprehensive evaluation of the vertical accuracy of ERA5 reanalysis data for boundary layer height and key meteorological variables, based on high-precision observational data from Baotou, located on the Mongolian Plateau, during the winter (January–March) and summer (July–August) months of 2021. Results indicate that ERA5 exhibits significant biases in horizontal wind speed, with deviations ranging from −5 to 8 m/s at 50 m, primarily driven by sandstorms in winter and convective weather in summer. The most pronounced errors occur below 500 m. Vertical wind speeds are consistently underestimated in both seasons, with biases reaching up to 1 m/s, particularly during active summer convection. ERA5 also struggles to reproduce low-level wind directions accurately. In winter, correlation coefficients range from 0.43 to 0.64 below 200 m and improve to above 0.7 at 500 m. In summer, correlation coefficients are lower, ranging from 0.3 to 0.5 below 200 m, with reduced accuracy at 500 m compared to winter. Temperature deviations increase above 2000 m, with a relative overestimation of 3% at 3000 m. Relative humidity is generally overestimated by 5–20% between 1000 and 2000 m in winter and by 10–30% in summer. For boundary layer heights, ERA5 overestimates daytime mixed-layer heights by up to 2000 m in summer and 500–800 m in winter. In contrast, ERA5 captures nocturnal stable boundary layer heights well during winter. This comprehensive evaluation of the vertical structure accuracy of ERA5 reanalysis data, conducted in a heavily industrialized city on the Mongolian Plateau, offers essential insights for improving meteorological studies and refining climate models in the region. The findings provide valuable reference data for enhancing weather forecasting and supporting climate change research, particularly in complex terrain areas. Full article

This paper reviews scenario generation techniques for modeling uncertainty in wind and photovoltaic (PV) power generation, a critical component as renewable energy integration into power systems grows. Scenario generation enables the simulation of variable power outputs under different weather conditions, serving as essential inputs for robust, stochastic, and distributionally robust optimization in system planning and operation. We categorize scenario generation methods into explicit and implicit approaches. Explicit methods rely on probabilistic assumptions and parameter estimation, which enable the interpretable yet parameterized modeling of power variability. Implicit methods, powered by deep learning models, offer data-driven scenario generation without predefined distributions, capturing complex temporal and spatial patterns in the renewable output. The review also addresses combined wind and PV power scenario generation, highlighting its importance for accurately reflecting correlated fluctuations in multi-site, interconnected systems. Finally, we address the limitations of scenario generation for wind and PV power integration planning and suggest future research directions. Full article

The paper analyzes the processes of emission and dispersion of particulate contaminants from a large point source emitter: a hard coal-fired power plant. Reference is made to the European Green Deal and its main objective of reducing anthropogenic particulate and greenhouse gas emissions. CHPP, Krakow Combined Heat and Power Plant, Poland, as described in the article, has a strong impact on the mechanisms that shape the microclimatic factors of the Krakow agglomeration. This combined heat and power plant provides heat and electricity for the city, while simultaneously emitting significant amounts of suspended particulate matter into the atmosphere. Due to the adverse impact of non-conventional energy sources on the natural environment and the increasing effects of climate warming, radical changes need to be implemented. The HYSPLIT (Hybrid Single-Particles Lagrangian Integrated Trajectories) model was used to track the movement of contaminated air masses. A 5-day episode of increased hourly concentrations of PM2.5 particulate matter contamination was selected to analyze the backward trajectories of air mass displacement. From 15 August 2022 to 19 August 2022, high 24-h particulate matter concentrations were recorded, measuring around 20 µg/m³. The HYSPLIT model, a unique tool in the precise identification of point sources of pollution and their impact on the air quality of the region, was used to analyze the influx of polluted air masses. A 5-day episode of increased hourly concentrations of PM2.5 pollutants was selected for the study, with values of approximately 20 µg/m³. It was found that low-pressure systems over the North Atlantic brought wet and variable weather conditions, while high-pressure systems in southern and eastern Europe, including Poland, provided stable and dry weather conditions. The simulation results were verified by analyzing synoptic maps of the study area. The image of the displacement of contaminated air masses obtained from the HYSPLIT model was found to be consistent with the synoptic maps, confirming the accuracy of the applied model. This means that the HYSPLIT model can be used to create maps of contaminant dispersion directions. Consequently, it was confirmed that modeling using the HYSPLIT model is an effective method for predicting the displacement directions of particulate contamination originating from coal combustion in a combined heat and power plant. Identifying circulation patterns and front zones during episodes of increased contaminant concentrations is strategic for effective weather monitoring, air quality management, and alerting the public to episodes of increased health risk in a large agglomeration. Full article

This exploratory study expected crop management and climatic factors to significantly influence the nutrient composition and amino acid digestibility of tannin-free sorghum grain determined in broilers of 3 wks of age. Using data from six tannin-free sorghum samples harvested across the southeast USA, Pearson correlations were analyzed (r ≥ |0.8|; p < 0.05). Standardized ileal amino acid digestibility (SIAD) was determined in a previous study using eight replicate cages with 13 birds per sorghum sample. SIAD values were correlated with nitrogen fertilization, yield, seeding rate, and climatic data obtained by surveying the crop growers and from weather stations. Nitrogen fertilization positively correlated with dry matter and starch. Yield was positively associated with SIAD, while seeding rate was negatively correlated with dry matter and Lys. Fiber, particular neutral detergent fiber, showed an inverse relationship with SIAD. No significant correlations with climatic factors were found, which was likely due to the close proximity of growing locations (r ≤ |0.8|; p > 0.05). Despite the limitations of a small sample size (n = 6) and genetic variability within and between each sorghum sample, these findings provide preliminary insights into managing sorghum cultivation to enhance its nutritional value for poultry. Future research should explore larger datasets, from further locations apart, and standardized data collection measurements to be able develop predictive models for grain quality improvement. Full article

The effects of climate change on the endocrine system are increasingly recognized. We aimed to evaluate endocrine clinicians’ perspectives on climate change awareness and knowledge, motivation for action, and the need for climate health curricula. We designed an online questionnaire with endocrine-specific questions about climate change, which was shared through social media and email. Study data were collected between 9/2022 and 11/2022. Analyses were primarily descriptive. There were 164 responses; 98% were physicians, with a median age of 41 years. The majority (95%) reported that climate change is happening; 52% reported that they are very worried. Knowledge about climate change and health was variable (6.7% very, 40% moderately, 35% modestly, 17.7% not at all), with variable concerns regarding patient effects. The top endocrine climate–health concerns were reduced exercise, malnutrition, and weather-related disruptions. Most respondents agreed that climate change and health topics should be integrated into medical education (72.8% strongly agree or agree). The three resources perceived as most helpful were continuing medical education, patient resources, and policy statements. Endocrine clinicians are aware of and worried about climate change, with varying levels of knowledge and concern about climate change and health effects. We also exposed an untapped interest in developing endocrine-specific climate and health curricula. Full article

Trees are indispensable to ecosystems, yet mortality rates have been increasing due to the abnormal changes in forest growth environments caused by frequent extreme weather events associated with global climate warming. Consequently, the need to monitor, assess, and predict tree mortality has become increasingly urgent to better address climate change and protect forest ecosystems. Over the past few decades, remote sensing has been widely applied to vegetation mortality observation due to its significant advantages. Here, we reviewed and analyzed the major research advancements in the application of remote sensing for tree mortality monitoring, using the Web of Science Core Collection database, covering the period from 1998 to the first half of 2024. We comprehensively summarized the use of different platforms (satellite and UAV) for data acquisition, the application of various sensors (multispectral, hyperspectral, and radar) as image data sources, the primary indicators, the classification models used in monitoring tree mortality, and the influence of tree mortality. Our findings indicated that satellite-based optical remote sensing data were the primary data source for tree mortality monitoring, accounting for 80% of existing studies. Time-series optical remote sensing data have emerged as a crucial direction for enhancing the accuracy of vegetation mortality monitoring. In recent years, studies utilizing airborne LiDAR have shown an increasing trend, accounting for 48% of UAV-based research. NDVI was the most commonly used remote sensing indicator, and most studies incorporated meteorological and climatic factors as environmental variables. Machine learning was increasingly favored for remote sensing data analysis, with Random Forest being the most widely used classification model. People are more focused on the impacts of tree mortality on water and carbon. Finally, we discussed the challenges in monitoring and evaluating tree mortality through remote sensing and offered perspectives for future developments. Full article

Beer produced by autochthonous microbial fermentation is a long-established craft beer style in Belgium that has now been implemented commercially in New Zealand. We used a metabarcoding approach to characterize the microbiome of 11 spontaneously fermented beers produced by a single brewery in Oamaru from 2016 to 2022. Key organic acid concentrations were also determined. Both bacterial and fungal populations varied considerably between vintages and between individual brews produced in 2020. Similarly, for organic acids, the concentrations of L-malic acid, succinic acid, and L-lactic acid statistically differed from one vintage to another. Moreover, a correlation between the concentrations of certain organic acids and microbial composition was inferred by ordination analyses. Through reference to publicly available climate data, humidity and maximum temperature seemed to enhance the abundance of Penicillium and Hanseniaspora in beer microbiota. However, comparison with previously published studies of Belgian lambic beers, similar Russian ales, and publicly available temperature data from these regions showed that the microbial populations of these were relatively stable despite greater extremes of weather. Our results suggest that while climatic variables may influence microbial populations during beer making that employs autochthonous fermentation in New Zealand, such variation is not evident where similar beers are produced in facilities with a long-established history of production. These findings have implications for lambic-style beer production in the context of global climate change, notably where microbial populations may lack environmental adaptation. Full article

When inter-arrival times between events follow an exponential distribution, this implies a Poisson frequency of events, as both models assume events occur independently and at a constant average rate. However, these assumptions are often violated in real-insurance applications. When the rate at which events occur changes over time, the exponential distribution becomes unsuitable. In this paper, we study the distribution of inter-arrival times of severe storms, which exhibit substantial variability, violating the assumption of a constant average rate. A new approach is proposed for modeling severe storm recurrence patterns using a finite mixture of log-normal distributions. This approach effectively captures both frequent, closely spaced storm events and extended quiet periods, addressing the inherent variability in inter-event durations. Parameter estimation is performed using the Expectation–Maximization algorithm, with model selection validated via the Bayesian information criterion (BIC). To complement the parametric approach, Kaplan–Meier survival analysis was employed to provide non-parametric insights into storm-free intervals. Additionally, a simulation-based framework estimates storm recurrence probabilities and assesses financial risks through probable maximum loss (PML) calculations. The proposed methodology is applied to the Billion-Dollar Weather and Climate Disasters dataset, compiled by the U.S. National Oceanic and Atmospheric Administration (NOAA). The results demonstrate the model’s effectiveness in predicting severe storm recurrence intervals, offering valuable tools for managing risk in the property and casualty insurance industry. Full article

The focus of this study is to investigate the underexplored operational effects of disengagements on the speed of an automated shuttle, providing novel insights into their disruptive impact on performance metrics. For this purpose, global positioning system data, disengagement records, weather reports, and roadway geometry data from an automated shuttle pilot program, from July to December 2023, at the University of North Carolina in Charlotte, were collected. The automated shuttle uses sensors for localization, navigation, and obstacle detection. A multi-level mixed-effects Gaussian regression model with a log-link function was employed to analyze the effect of disengagement events on the automated shuttle speed, while accounting for control variables such as roadway geometry, weather conditions, time-of-the-day, day-of-the-week, and number of intermediate stops. When these variables are controlled, disengagements significantly reduce the automated shuttle speed, with the expected log of speed decreasing by 0.803 units during such events. This reduction underscores the disruptive impact of disengagements on the automated shuttle’s performance. The analysis revealed substantial variability in the effect of disengagements across different route segments, suggesting that certain segments, likely due to varying traffic conditions, road geometries, and traffic control characteristics, pose greater challenges for autonomous navigation. By employing a multi-level mixed-effects model, this study provides a robust framework for quantifying the operational impact of disengagements. The findings serve as vital insights for advancing the reliability and safety of autonomous systems through targeted improvements in technology and infrastructure. Full article

Enabled by recent technological advances and the substantial growth of the sharing economy, electric bike-sharing (EBS) has experienced rapid growth in medium-sized Chinese cities, yet its impact on for-hire vehicle (FHV) services remains insufficiently studied. Using a six-month longitudinal dataset from Yancheng, a representative medium-sized city in China, we employ an instrumental variable method to address potential endogeneity and provide quantitative empirical analysis. The analysis identifies a significant substitution effect, where a 1% increase in EBS trips corresponds to a 0.810% decline in FHV ridership. Through heterogeneity analyses, this study reveals that the substitutive effect of EBS is stronger in central downtown, which has denser infrastructure, while its impact diminishes in peripheral districts. Furthermore, unfavorable weather conditions mitigate the substitutive effect, as users increasingly rely on FHVs for their reliability and comfort during unfavorable conditions. The findings of this study highlight the necessity of integrating EBS into the electrified shared mobility ecosystem in a balanced manner to prevent disruptions to the existing transportation network and provide valuable guidance for sustainable and stable transportation planning in medium-sized cities and similar urban contexts. Full article

Meteor radar is a widely used technique for measuring wind in the mesosphere and lower thermosphere, with the key advantage of being unaffected by terrestrial weather conditions, thus enabling continuous operation. In all-sky interferometric meteor radar systems, amplitude and phase consistencies between multiple channels exhibit dynamic variations over time, which can significantly degrade the accuracy of wind measurements. Despite the inherently dynamic nature of these inconsistencies, the majority of existing research predominantly employs static calibration methods to address these issues. In this study, we propose a dynamic adaptive calibration method that combines normalized least mean square and correlation algorithms, integrated with hardware design. We further assess the effectiveness of this method through numerical simulations and practical implementation on an independently developed meteor radar system with a five-channel receiver. The receiver facilitates the practical application of the proposed method by incorporating variable gain control circuits and high-precision synchronization analog-to-digital acquisition units, ensuring initial amplitude and phase consistency accuracy. In our dynamic calibration, initial coefficients are determined using a sliding correlation algorithm to assign preliminary weights, which are then refined through the proposed method. This method maximizes cross-channel consistencies, resulting in amplitude inconsistency of <0.0173 dB and phase inconsistency of <0.2064°. Repeated calibration experiments and their comparison with conventional static calibration methods demonstrate significant improvements in amplitude and phase consistency. These results validate the potential of the proposed method to enhance both the detection accuracy and wind inversion precision of meteor radar systems. Full article

Accurate safflower recognition is a critical research challenge in the field of automated safflower harvesting. The growing environment of safflowers, including factors such as variable weather conditions in unstructured environments, shooting distances, and diverse morphological characteristics, presents significant difficulties for detection. To address these challenges and enable precise safflower target recognition in complex environments, this study proposes an improved safflower detection model, WED-YOLO, based on YOLOv8n. Firstly, the original bounding box loss function is replaced with the dynamic non-monotonic focusing mechanism Wise Intersection over Union (WIoU), which enhances the model’s bounding box fitting ability and accelerates network convergence. Then, the upsampling module in the network’s neck is substituted with the more efficient and versatile dynamic upsampling module, DySample, to improve the precision of feature map upsampling. Meanwhile, the EMA attention mechanism is integrated into the C2f module of the backbone network to strengthen the model’s feature extraction capabilities. Finally, a small-target detection layer is incorporated into the detection head, enabling the model to focus on small safflower targets. The model is trained and validated using a custom-built safflower dataset. The experimental results demonstrate that the improved model achieves Precision (P), Recall (R), mean Average Precision (mAP), and F₁ score values of 93.15%, 86.71%, 95.03%, and 89.64%, respectively. These results represent improvements of 2.9%, 6.69%, 4.5%, and 6.22% over the baseline model. Compared with Faster R-CNN, YOLOv5, YOLOv7, and YOLOv10, the WED-YOLO achieved the highest mAP value. It outperforms the module mentioned by 13.06%, 4.85%, 4.86%, and 4.82%, respectively. The enhanced model exhibits superior precision and lower miss detection rates in safflower recognition tasks, providing a robust algorithmic foundation for the intelligent harvesting of safflowers. Full article

The geostationary meteorological satellite Fengyun-4A (FY-4A) has rapidly advanced, generating abundant high spatiotemporal resolution atmospheric precipitable water vapor (PWV) products. However, remote sensing satellites are vulnerable to weather conditions, and these latest operational PWV products still require systematic validation. This study presents a comprehensive evaluation of FY-4A PWV products by separately using PWV data retrieved from radiosondes (RS) and the Global Navigation Satellite System (GNSS) from 2019 to 2022 in China and the surrounding regions. The overall results indicate a significant consistency between FY-4A PWV and RS PWV as well as GNSS PWV, with mean biases of 7.21 mm and −8.85 mm, and root mean square errors (RMSEs) of 7.03 mm and 3.76 mm, respectively. In terms of spatial variability, the significant differences in mean bias and RMSE were 6.50 mm and 2.60 mm between FY-4A PWV and RS PWV in the northern and southern subregions, respectively, and 5.36 mm and 1.73 mm between FY-4A PWV and GNSS PWV in the northwestern and southern subregions, respectively. The RMSE of FY-4A PWV generally increases with decreasing latitude, and the bias is predominantly negative, indicating an underestimation of water vapor. Regarding temporal differences, both the monthly and daily biases and RMSEs of FY-4A PWV are significantly higher in summer than in winter, with daily precision metrics in summer displaying pronounced peaks and irregular fluctuations. The classic seasonal, regional adjustment model effectively reduced FY-4A PWV deviations across all regions, especially in the NWC subregion with low water vapor distribution. In summary, the accuracy metrics of FY-4A PWV show distinct spatiotemporal variations compared to RS PWV and GNSS PWV, and these variations should be considered to fully realize the potential of multi-source water vapor applications. Full article

Africa is grappling with severe food security challenges driven by population growth, climate change, land degradation, water scarcity, and socio-economic factors such as poverty and inequality. Climate variability and extreme weather events, including droughts, floods, and heatwaves, are intensifying food insecurity by reducing agricultural productivity, water availability, and livelihoods. This study examines the projected threats to food security in Africa, focusing on changes in temperature, precipitation patterns, and the frequency of extreme weather events. Using an Exponential Growth Model, we estimated the population from 2020 to 2050 across Africa’s five sub-regions. The analysis assumes a 5% reduction in crop yields for every degree of warming above historical levels, with a minimum requirement of 225 kg of cereals per person per year. Climate change is a critical factor in Africa’s food systems, with an average temperature increase of approximately +0.3 °C per decade. By 2050, the total food required to meet the 2100-kilocalorie per adult equivalent per day will rise to 558.7 million tons annually, up from 438.3 million tons in 2020. We conclude that Africa’s current food systems are unsustainable, lacking resilience to climate shocks and relying heavily on rain-fed agriculture with inadequate infrastructure and technology. We call for a transformation in food systems through policy reform, technological and structural changes, solutions to land degradation, and proven methods of increasing crop yields that take the needs of communities into account. Full article