Damage to tropical forests caused by cyclones is driven by wind speed but mediated by topographical exposure and tree characteristics.

Each year, an average of 45 tropical cyclones affect coastal areas and potentially impact forests. The proportion of the most intense cyclones has increased over the past four decades and is predicted to continue to do so. Yet, it remains uncertain how topographical exposure and tree characteristics can mediate the damage caused by increasing wind speed. Here, we compiled empirical data on the damage caused by 11 cyclones occurring over the past 40 years, from 74 forest plots representing tropical regions worldwide, encompassing field data for 22,176 trees and 815 species. We reconstructed the wind structure of those tropical cyclones to estimate the maximum sustained wind speed (MSW) and wind direction at the studied plots. Then, we used a causal inference framework combined with Bayesian generalised linear mixed models to understand and quantify the causal effects of MSW, topographical exposure to wind (EXP), tree size (DBH) and species wood density (ρ) on the proportion of damaged trees at the community level, and on the probability of snapping or uprooting at the tree level. The probability of snapping or uprooting at the tree level and, hence, the proportion of damaged trees at the community level, increased with increasing MSW, and with increasing EXP accentuating the damaging effects of cyclones, in particular at higher wind speeds. Higher ρ decreased the probability of snapping and to a lesser extent of uprooting. Larger trees tended to have lower probabilities of snapping but increased probabilities of uprooting. Importantly, the effect of ρ decreasing the probabilities of snapping was more marked for smaller than larger trees and was further accentuated at higher MSW. Our work emphasises how local topography, tree size and species wood density together mediate cyclone damage to tropical forests, facilitating better predictions of the impacts of such disturbances in an increasingly windier world.

Albatrosses employ orientation and routing strategies similar to yacht racers.

The way goal-oriented birds adjust their travel direction and route in response to wind significantly affects their travel costs. This is expected to be particularly pronounced in pelagic seabirds, which utilize a wind-dependent flight style called dynamic soaring. Dynamic soaring seabirds in situations without a definite goal, e.g. searching for prey, are known to preferentially fly with crosswinds or quartering-tailwinds to increase the speed and search area, and reduce travel costs. However, little is known about their reaction to wind when heading to a definite goal, such as homing. Homing tracks of wandering albatrosses (Diomedea exulans) vary from beelines to zigzags, which are similar to those of sailboats. Here, given that both albatrosses and sailboats travel slower in headwinds and tailwinds, we tested whether the time-minimizing strategies used by yacht racers can be compared to the locomotion patterns of wandering albatrosses. We predicted that when the goal is located upwind or downwind, albatrosses should deviate their travel directions from the goal on the mesoscale and increase the number of turns on the macroscale. Both hypotheses were supported by track data from albatrosses and racing yachts in the Southern Ocean confirming that albatrosses qualitatively employ the same strategy as yacht racers. Nevertheless, albatrosses did not strictly minimize their travel time, likely making their flight robust against wind fluctuations to reduce flight costs. Our study provides empirical evidence of tacking in albatrosses and demonstrates that man-made movement strategies provide a new perspective on the laws underlying wildlife movement.

Effects of extreme meteorological factors and high air pollutant concentrations on the incidence of hand, foot and mouth disease in Jining, China.

Background: The evidence on the effects of extreme meteorological conditions and high air pollution levels on incidence of hand, foot and mouth disease (HFMD) is limited. Moreover, results of the available studies are inconsistent. Further investigations are imperative to elucidate the specific issue. Methods: Data on the daily cases of HFMD, meteorological factors and air pollution were obtained from 2017 to 2022 in Jining City. We employed distributed lag nonlinear model (DLNM) incorporated with Poisson regression to explore the impacts of extreme meteorological conditions and air pollution on HFMD incidence. Results: We found that there were nonlinear relationships between temperature, wind speed, PM2.5, SO2, O3 and HFMD. The cumulative risk of extreme high temperature was higher at the 95th percentile (P95th) than at the 90th percentile(P90th), and the RR values for both reached their maximum at 10-day lag (P95th RR = 1.880 (1.261-2.804), P90th RR = 1.787 (1.244-2.569)), the hazardous effect of extreme low temperatures on HFMD is faster than that of extreme high temperatures. The cumulative effect of extreme low wind speeds reached its maximum at 14-day lag (P95th RR = 1.702 (1.389-2.085), P90th RR = 1.498(1.283-1.750)). The cumulative effect of PM2.5 concentration at the P90th was largest at 14-day lag (RR = 1.637 (1.069-2.506)), and the cumulative effect at the P95th was largest at 10-day lag (RR = 1.569 (1.021-2.411)). High SO2 concentration at the P95th at 14-day lag was associated with higher risk for HFMD (RR: 1.425 (1.001-2.030)). Conclusion: Our findings suggest that high temperature, low wind speed, and high concentrations of PM2.5 and SO2 are associated with an increased risk of HFMD. This study not only adds insights to the understanding of the impact of extreme meteorological conditions and high levels of air pollutants on HFMD incidence but also holds practical significance for the development and enhancement of an early warning system for HFMD.

Research of biomimetic corrugation on the blade flutter suppression in large-scale wind turbine systems.

Aiming at the blade flutter of large horizontal-axis wind turbines, a method by utilizing biomimetic corrugation to suppress blade flutter is first proposed. By extracting the dragonfly wing corrugation, the biomimetic corrugation airfoil is constructed, finding that mapping corrugation to the airfoil pressure side has better aerodynamic performance. The influence of corrugation type, amplitudeλ, and intensity on airfoil flutter is analyzed using orthogonal experiment, which determines that theλhas the greatest influence on airfoil flutter. Based on the fluctuation range of the moment coefficient ΔCm, the optimal airfoil flutter suppression effect is obtained when the type is III,λ= 0.6, and intensity is denser (n= 13). The effective corrugation layout area in the chord direction is determined to be the leading edge, and the ΔCmof corrugation airfoil is reduced by 7.405%, compared to the original airfoil. The application of this corrugation to NREL 15 MW wind turbine 3D blades is studied, and the influence of corrugation layout length in the blade span direction on the suppressive effect is analyzed by fluid-structure interaction. It is found that when the layout length is 0.85 R, the safety marginSfreaches a maximum value of 0.3431 Hz, which is increased 2.940%. The results show that the biomimetic corrugated structure proposed in this paper can not only improve the aerodynamic performance by changing the local flow field on the surface of the blade, but also increase the structural stiffness of the blade itself, and achieve the effect of flutter suppression.

Comprehensive analysis of air pollution and the influence of meteorological factors: a case study of adiyaman province.

Adiyaman, a city recently affected by an earthquake, is facing significant air pollution challenges due to both anthropogenic activities and natural events. The sources of air pollution have been investigated using meteorological variables. Elevated southerly winds, especially prominent in spring and autumn, significantly contribute to dust transport, leading to a decline in local air quality as detected by the HYSPLIT model. Furthermore, using Suomi-NPP Thermal Anomaly satellite product, it is detected and analyzed for crop burning activities. Agricultural practices, including stubble burning, contribute to the exacerbation of PM10 pollution during the summer months, particularly when coupled with winds from all directions except the north. In fall and winter months, heating is identified as the primary cause of pollution. The city center located north of the station is the dominant source of pollution throughout all seasons. The study established the connection between air pollutants and meteorological variables. Furthermore, the Spearman correlation coefficients reveal associations between PM10 and SO2, indicating moderate positive correlations under pressure conditions (r = 0.35, 0.52). Conversely, a negative correlation is observed with windspeed (r = -0.35, -0.50), and temperature also exhibits a negative correlation (r = -0.39, -0.54). During atmospheric conditions with high pressure, PM10 and SO2 concentrations are respectively 41.2% and 117.2% higher. Furthermore, pollutant concentration levels are 29.2% and 53.3% higher on days with low winds. Last, practical strategies for mitigating air pollution have been thoroughly discussed and proposed. It is imperative that decision-makers engaged in city planning and renovation give careful consideration to the profound impact of air pollution on both public health and the environment, particularly in the aftermath of a recent major earthquake.

Influence of building spatial patterns on wind environment and air pollution dispersion inside an industrial park based on CFD simulation.

The "spatial pattern-wind environment-air pollution" within building clusters is closely interconnected, where different spatial pattern parameters may have varying degrees of impact on the wind environment and pollutant dispersion. Due to the complex spatial structure within industrial parks, this complexity may lead to the accumulation and retention of air pollutants within the parks. Therefore, to alleviate the air pollution situation in industrial parks in China and achieve the circular transformation and construction of parks, this study takes Hefei Circular Economy Demonstration Park as the research object. The microscale Fluent model in computational fluid dynamics (CFD) is used to finely simulate the wind flow field and the diffusion process of pollutants within the park. The study analyzes the triad relationship and influence mechanism of "spatial pattern-wind environment-air pollution" within the park and studies the influence of different spatial pattern parameters on the migration and diffusion of pollutants. The results show a significant negative correlation between the content of pollutants and wind speed inside the industrial park. The better the wind conditions, the higher the air quality. The spatial morphology parameters of the building complex are the main influences on the condition of its internal wind environment. Building coverage ratio and degree of enclosure have a significant negative correlation with wind conditions. Maintaining them near 0.23 and 0.37, respectively, is favorable to the quality of the surrounding environment. Moreover, the average height of the building is positively correlated with the wind environment condition. The rate of transport and dissipation of pollutants gradually increases as the average building height reaches 16 m. Therefore, a reasonable building planning strategy and arrangement layout can effectively improve the wind environment condition inside the park, thus alleviating the pollutant retention situation. The obtained results serve as a theoretical foundation for optimizing morphological structure design within urban industrial parks.

Shifting from techno-economic to socio-ecological priorities: Incorporating landscape preferences and ecosystem services into the siting of renewable energy infrastructure.

This study examines the siting scenarios for renewable energy installations (REI) in a mountainous region of Europe (Switzerland), incorporating the external costs of ecosystem services and, innovatively, social preferences. This approach challenges the prevalent techno-economic siting paradigm, which often overlooks these externalities. To minimize the external costs of the scenarios while maximizing energy yield, Marxan, an optimization software, was employed. The energy target for all scenarios is set at 25 TWh/a, stemming from the energy gap anticipated due to the phase-out of Swiss nuclear reactors by 2050. This target is met using renewable energy infrastructure such as wind, roof-mounted photovoltaic, and ground-mounted photovoltaic systems. By integrating social preferences into the optimization, this study showcases a promising implementation that transcends the software's intended applications. It complements techno-economic approaches and offers alternative decision-making avenues. The conventional "roof first" strategy proved ineffective in preventing extensive land use for the development of new renewable energy infrastructure. Strategies incorporating ground-mounted photovoltaic infrastructure were more spatially, ecologically, and socially efficient than those without. The strategy optimized for energy yield exhibited the highest spatial efficiency but incurred significant ecosystem service costs and, surprisingly, had low social costs. In contrast, the strategy prioritizing ecosystem services was the most efficient in terms of ecosystem service costs but had elevated social costs and was spatially less efficient than other strategies. The strategy optimized for social preferences incurred the lowest social costs and excelled in spatial efficiency and ecosystem service costs. Notably, this strategy employed a limited number of planning units linked to both high ecosystem service and social costs. The findings underscore that incorporating social preferences significantly enhances the evaluation of siting options. This inclusion allows for the social acceptance of investments to be factored into costs, facilitating more informed and inclusive decisions.

Improving new energy subsidy efficiency considering learning effect: A case study on wind power.

Subsidy policies are instrumental in driving the development of new energy. However, the effective allocation of new energy subsidies over time is challenging given fiscal constraints. This study addresses this challenge by considering the learning effect associated with the new energy industry. A two-stage dynamic programming model is proposed to capture the investment decision-making process of companies under new energy subsidy policies and government subsidy setups. Theoretical findings suggest that company investment decisions in new energy are influenced by a guiding principle: The subsidy rate should be negatively correlated with the variation rate of production scale increment (VRPSI). We calibrate this investment decision principle using wind power data from 14 countries. According to this principle, excessive subsidy rates may result in a low VRPSI, thereby diminishing future investment profitability in the new energy industry and leading to subsidy inefficiency. Upon investigating the efficiency of annual subsidy allocation, we find that the subsidy rates were potentially set too high in 2014, 2016, and 2017. Furthermore, the government should exercise caution regarding an inefficient subsidy pattern whereby companies invest in new energy only when the subsidy rate exceeds a certain threshold, neglecting traditional power sources. It is crucial to note that although this study uses wind power industry data for calibration and simulation, the theoretical model can be broadly applied to other new energy industries and emerging industries with increasing marginal net profit.

[Analysis of the Spatiotemporal Distribution of Algal Blooms and Its Driving Factors in Chaohu Lake Based on Multi-source Datasets].

Eutrophication and harmful algae blooms are one of the common ecological and environmental problems faced by freshwater lakes all over the world. As a typical inland freshwater lake, Chaohu Lake exhibits a high level of eutrophication and algae blooms year-round and shows a spatiotemporal difference in different regions of the lake. In order to understand the basic regularity of the development and outbreak of algal blooms in Chaohu Lake, the data from the comprehensive water observation platform and remote sensing were integrated to obtain the spatiotemporal distribution of algal blooms from 2015 to 2020. Then, an evaluation model based on Boosted Regression Trees (BRT) was constructed to quantitatively assess the importance and interactions of various environmental factors on algal blooms at different stages. The results indicated that:① The occurrence of algal blooms in Chaohu Lake exhibited significant seasonal variations, with the cyanobacteria beginning to recover in spring and bring about a light degree of algal blooms in the western and coastal areas of Chaohu Lake. The density of cyanobacteria reached its maximum in summer and autumn, accompanied by moderate and severe degrees of algal bloom outbreaks. ② During the non-outbreak period, the variation in the cyanobacteria density was greatly affected by physical and chemical factors, which explained 80.3% of the variance in the change in cyanobacteria density. The high concentrations of dissolved oxygen content in the water column and the weak alkalinity (7.2-7.6) and appropriate water temperature (about 3℃) provided a favorable environmental condition for the breeding and growth of cyanobacteria. In addition, the onset of algal blooms was closely related to the air temperature steadily passing through the threshold. According to the statistics, the date of first outbreak of algal blooms in Chaohu Lake was 11 days or so after the air temperature steadily remained above 7℃. ③ During the outbreak period, the occurrence of algal blooms was influenced by the combination of cyanobacterial biomass and meteorological conditions such as temperature, wind speed, and sunshine duration. The cumulative contribution ratio of the four factors was as high as 95%, and each factor had an optimal interval conductive to the outbreak of algal blooms. Furthermore, the results of multi-factor interaction analysis indicated a larger probability of the outbreak of algal blooms in Chaohu Lake under the combined effect of high cyanobacteria density, suitable temperature, and the breeze. This study analyzed and revealed the spatiotemporal characteristics and the dominant influencing factors of algal blooms in Chaohu Lake at different stages, which could provide the scientific basis for the prediction, early warning, and disposal of algal blooms under the context of climate change.

Revisiting the contribution of different factors in determining the changes in potential evapotranspiration over China.

In this paper, a daily gridded observation data across China from 1961 to 2022 were used to calculate daily potential evapotranspiration (PET). The observed variables included daily temperature, sunshine hours, average wind speed, and average relative humidity. PET was determined using the Penman-Monteith method recommended by the Food and Agriculture Organization (FAO). The long-term trend of PET was investigated in six regions of China during different seasons. To further compressed the influence of various meteorological factors on the PET trend, the contribution of each meteorological element to the long-term trend of PET was analyzed. The results indicate the following: (1) PET reaches its peak during summer which values from 145 to 640 mm, while it is lowest during winter from 21 to 244 mm. (2) The spatial patterns of PET trend changes are relatively similar across the four seasons, characterized by a decrease in the eastern regions and an increase in the western regions. The reduction is most significant during the summer and the range of trend is from -2.04 to 1.48 mm/day, while the increase becomes more pronounced in the winter which trend is from -0.34 to 0.53 mm/day. (3) The contribution of factors varies significantly across different regions. In spring and autumn, RH and U have little difference in contribution from other factors. But tsun is varies different from regions, the contribution value is largest in the northwest and smallest in the northeast. However, during summer, tsun become the most significant contributor in the YZ and SE regions, while in winter, Tm emerges as the most significant contributor to the PET trend in all six subregions. In SW, the contribution from U2 is the smallest in all seasons, with RH and Tm being the two crucial factors determining the PET trend in this region.

Applicability of the generalized wind profile model over mountainous forests.

The analytical equation based on Monin-Obukhov (M-O) similarity theory (i.e., wind profile equation) has been adopted since 1970s for using in the prediction of wind vertical profile over flat terrains, which is mature and accurate. However, its applicability over complex terrains remains unknown. This applicability signifies the accuracy of the estimations of aerodynamic parameters for the boundary layer of non-flat terrain, such as zero-displacement height (d) and aerodynamic roughness length (z0), which will determine the accuracy of frequency correction and source area analysis in calculating carbon, water, and trace gas fluxes based on vorticity covariance method. Therefore, the validation of wind profile model in non-flat terrain is the first step to test whether the flux model needs improvement. We measured three-dimensional wind speed data by using the Ker Towers (three towers in a watershed) at Qingyuan Forest CERN in the Mountainous Region of east Liaoning Province, and compared them with data from Panjin Agricultural Station in the Liaohe Plain, to evaluate the applicability of a generalized wind profile model based on the Monin-Obukhov similarity theory on non-flat terrain. The results showed that the generalized wind profile model could not predict wind speeds accurately of three flux towers separately located in different sites, indicating that wind profile model was not suitable for predicting wind speeds in complex terrains. In the leaf-off and leaf-on periods, the coefficient of determination (R2) between observed and predicted wind speeds ranged from 0.12 to 0.30. Compared to measured values, the standard error of the predicted wind speeds was high up to 2 m·s-1. The predicted wind speeds were high as twice as field-measured wind speed, indicating substantial overestimation. Nevertheless, this model correctly predicted wind speeds in flat agricultural landscape in Panjin Agricultural Station. The R2 between observed wind speeds and predicted wind speed ranged from 0.90 to 0.93. The standard error between observed and predicted values was only 0.5 m·s-1. Results of the F-test showed that the root-mean-square error of the observed and predicted wind speeds in each secondary forest complex terrain was much greater than that in flat agricultural landscape. Terrain was the primary factor affecting the applicability of wind profile model, followed by seasonality (leaf or leafless canopy). The wind profile model was not applicable to the boundary-layer flows over forest canopies in complex terrains, because the d was underestimated or both the d and z0 were underestimated, resulting in inaccurate estimation of aerodynamic height.

Metals and polycyclic aromatic hydrocarbons pollutants in industrial parks under valley landforms in Tibetan Plateau: Spatial pattern, ecological risk and interaction with soil microorganisms.

The spatial patterns of pollutants produced by industrial parks are affected by many factors, but the interactions among polycyclic aromatic hydrocarbons (PAHs), metals, and soil microorganisms in the valley landforms of the Tibetan Plateau are poorly understood. Thus, this study systematically investigated the distribution and pollution of metals and PAHs in soil around an industrial park in the typical valley landform of the Tibetan Plateau and analyzed and clarified the interaction among metals, PAHs, and microorganisms. The results were as follows: metal and PAH concentrations were affected by wind direction, especially WN-ES and S-N winds; Cd (2.86-54.64 mg·kg-1) had the highest soil concentrations of the metals screened, followed by variable concentrations of Cu, Pb, and Zn; the pollution levels of metals and PAHs in the S-N wind direction were lower than those in the WN-ES wind direction; the Cd content of Avena sativa in the agricultural soil around the factory exceeded its enrichment ability and food safety standards; the closer to the center of the park, the higher the ecological risk of PAHs; and the TEQ and MEQ values of the PAHs were consistent with their concentration distributions. The results of the soil microbial diversity and co-occurrence network in the dominant wind direction showed that metal and PAH pollution weakened the robustness of soil microbial communities. Additionally, the diversity and robustness of soil microbial communities at the S wind site were higher than those at the ES wind site, which might be attributed to the lower metal content of the former than the latter, which plays a negative role in the biodegradation of PAHs. The results of this study provide insights into the site selection, pollutant supervision, and environmental remediation of industrial parks in typical landforms.

Co-processing of end-of-life wind turbine blades in portland cement production.

The objective of this paper is to evaluate the feasibility of co-processing wind turbine blade (WTB) material in cement manufacturing to provide an end-of-life means to divert the solid waste of decommissioned WTBs from landfills. Many WTBs consist primarily of glass fiber reinforced thermoset polymers that are difficult to recover or recycle. Portland cement is produced world-wide in large quantities, requiring immense quantities of raw materials (mostly calcium oxide and silicon oxide) and kiln temperatures approaching 1,450 °C. This work contributes analyses of WTB material composition, and predicts the energy provided through the combustible components of the WTBs and raw material contributions provided by incorporating the incombustible components of the WTBs to produce cement. Approximately 40 to 50 % of the WTB material will contribute as fuel to cement production, and approximately 50 to 60 % of the WTB material is expected to be incombustible. One tonne of WTB material can displace approximately 0.4 to 0.5 tonne of coal, while also contributing approximately 0.1 tonne of calcium oxide and 0.3 tonne of silicon oxide as raw material to the cement production process. The glass fiber WTB tested had an average boron content of 4.5 % in the ash. The effects of this high boron content on the cement and its production process should be evaluated. Co-processing WTBs in cement plants will slightly reduce combustion-related CO2 emissions due to avoided calcination. It seems feasible to co-process glass-fiber reinforced WTBs in cement production as WTBs provide suitable raw materials and compatible fuel for this process.

Migration and transformation behaviors of antibiotics in water-sediment system under simulated light and wind waves.

Antibiotics can generally be detected in the water-sediment systems of lakes. However, research on the migration and transformation of antibiotics in water-sediment systems based on the influences of light and wind waves is minimal. To address this research gap, we investigated the specific impacts of light and wind waves on the migration and transformation of three antibiotics, norfloxacin (NOR), trimethoprim (TMP), and sulfamethoxazole (SMX), under simulated light and wind waves disturbance conditions in a water-sediment system from Taihu Lake, China. In the overlying water, NOR was removed the fastest, followed by TMP and SMX. Compared to the no wind waves groups, the disturbance of big wind waves reduced the proportion of antibiotics in the overlying water. The contributions of light and wind waves to TMP and SMX degradation were greater than those of microbial degradation. However, the non-biological and biological contributions of NOR to degradation were almost equal. Wind waves had a significant impact on the microbial community changes in the sediment, especially in Methylophylaceae. These results verified the influence of light and wind waves on the migration and transformation of antibiotics, and provide assistance for the risk of antibiotic occurrence in water and sediments.

Enhancing the control of doubly fed induction generators using artificial neural networks in the presence of real wind profiles.

This study tackles the complex task of integrating wind energy systems into the electric grid, facing challenges such as power oscillations and unreliable energy generation due to fluctuating wind speeds. Focused on wind energy conversion systems, particularly those utilizing double-fed induction generators (DFIGs), the research introduces a novel approach to enhance Direct Power Control (DPC) effectiveness. Traditional DPC, while simple, encounters issues like torque ripples and reduced power quality due to a hysteresis controller. In response, the study proposes an innovative DPC method for DFIGs using artificial neural networks (ANNs). Experimental verification shows ANNs effectively addressing issues with the hysteresis controller and switching table. Additionally, the study addresses wind speed variability by employing an artificial neural network to directly control reactive and active power of DFIG, aiming to minimize challenges with varying wind speeds. Results highlight the effectiveness and reliability of the developed intelligent strategy, outperforming traditional methods by reducing current harmonics and improving dynamic response. This research contributes valuable insights into enhancing the performance and reliability of renewable energy systems, advancing solutions for wind energy integration complexities.

The genomic history and global migration of a windborne pest.

Many insect pests, including the brown planthopper (BPH), undergo windborne migration that is challenging to observe and track. It remains controversial about their migration patterns and largely unknown regarding the underlying genetic basis. By analyzing 360 whole genomes from around the globe, we clarify the genetic sources of worldwide BPHs and illuminate a landscape of BPH migration showing that East Asian populations perform closed-circuit journeys between Indochina and the Far East, while populations of Malay Archipelago and South Asia undergo one-way migration to Indochina. We further find round-trip migration accelerates population differentiation, with highly diverged regions enriching in a gene desert chromosome that is simultaneously the speciation hotspot between BPH and related species. This study not only shows the power of applying genomic approaches to demystify the migration in windborne migrants but also enhances our understanding of how seasonal movements affect speciation and evolution in insects.

Achieving Decentralized, Electrified, and Decarbonized Ammonia Production.

The rapid reduction in the cost of renewable energy has motivated the transition from carbon-intensive chemical manufacturing to renewable, electrified, and decarbonized technologies. Although electrified chemical manufacturing technologies differ greatly, the feasibility of each electrified approach is largely related to the energy efficiency and capital cost of the system. Here, we examine the feasibility of ammonia production systems driven by wind and photovoltaic energy. We identify the optimal regions where wind and photovoltaic electricity production may be able to meet the local demand for ammonia-based fertilizers and set technology targets for electrified ammonia production. To compete with the methane-fed Haber-Bosch process, electrified ammonia production must reach energy efficiencies of above 20% for high natural gas prices and 70% for low natural gas prices. To account for growing concerns regarding access to water, geospatial optimization considers water stress caused by new ammonia facilities, and recommendations ensure that the identified regions do not experience an increase in water stress. Reducing water stress by 99% increases costs by only 1.4%. Furthermore, a movement toward a more decentralized ammonia supply chain driven by wind and photovoltaic electricity can reduce the transportation distance for ammonia by up to 76% while increasing production costs by 18%.

Risk assessment of deep-sea floating offshore wind power projects based on hesitant fuzzy linguistic term set considering trust relationship among experts.

The development of deep-sea floating offshore wind power (FOWP) is the key to fully utilizing water resources to enhance wind resources in the years ahead, and then the project is still in its initial stage, and identifying risks is a crucial step before promoting a significant undertaking. This paper proposes a framework for identifying risks in deep-sea FOWP projects. First, this paper identifies 16 risk criteria and divides them into 5 groups to establish a criteria system. Second, hesitant fuzzy linguistic term set (HFLTS) and triangular fuzzy number (TFN) are utilized to gather and describe the criterion data to ensure the robustness and completeness of the criterion data. Third, extending the method for removal effects of criteria (MEREC) to the HFLTS environment through the conversion of TFNs, under the influence of subjective preference and objective fairness, a weighting method combining analytic network process (ANP) and MEREC is utilized to calculate criteria weights, and the trust relationship and consistency between experts are used to calculate the expert weights to avoid the subjective weighting given by experts arbitrariness. Fourth, the study's findings indicated that the overall risk level of the deep-sea FOWP projects is "medium." Fifth, sensitivity and comparative analyses were conducted to test the reliability of the assessment outcomes. lastly, this research proposes risk management measures for the deep-sea FOWP project's establishment from economic, policy, technology, environment, and management aspects.

Massive seasonal high-altitude migrations of nocturnal insects above the agricultural plains of East China.

Long-distance migrations of insects contribute to ecosystem functioning but also have important economic impacts when the migrants are pests or provide ecosystem services. We combined radar monitoring, aerial sampling, and searchlight trapping, to quantify the annual pattern of nocturnal insect migration above the densely populated agricultural lands of East China. A total of ~9.3 trillion nocturnal insect migrants (15,000 t of biomass), predominantly Lepidoptera, Hemiptera, and Diptera, including many crop pests and disease vectors, fly at heights up to 1 km above this 600 km-wide region every year. Larger migrants (>10 mg) exhibited seasonal reversal of movement directions, comprising northward expansion during spring and summer, followed by southward movements during fall. This north-south transfer was not balanced, however, with southward movement in fall 0.66× that of northward movement in spring and summer. Spring and summer migrations were strongest when the wind had a northward component, while in fall, stronger movements occurred on winds that allowed movement with a southward component; heading directions of larger insects were generally close to the track direction. These findings indicate adaptations leading to movement in seasonally favorable directions. We compare our results from China with similar studies in Europe and North America and conclude that ecological patterns and behavioral adaptations are similar across the Northern Hemisphere. The predominance of pests among these nocturnal migrants has severe implications for food security and grower prosperity throughout this heavily populated region, and knowledge of their migrations is potentially valuable for forecasting pest impacts and planning timely management actions.