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Xinjiang, located in the arid region of the northwest, is one of the areas most sensitive to global changes. The Kaidu River Basin, situated in the heart of Xinjiang, is one of the sources of China's largest inland river—the Tarim River. The Kaidu River not only bears the responsibility for supplying water for industrial use and agricultural production and people's daily life in the basin, but also plays a crucial role in ecological water supply to the Tarim River. Studying and analyzing the characteristics and trends of meteorological condition in the future under climate change can provide important references and a basis for a deeper understanding of changes in the hydrological process and water resources in the basin. Therefore, this paper selects seven precipitation bias correction methods and four temperature bias correction methods to adjust the precipitation and temperature output data of eight general circulation models of the Sixth Coupled Model Intercomparison Project (CMIP6) within the Kaidu River Basin. The applicability of different bias correction methods in the study area is evaluated, and based on the corrected future meteorological data and calculated extreme meteorological index, the trends of meteorological data (precipitation, temperature) in the future period (2025–2050) under four SSP scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) in the Kaidu River Basin are analyzed. The results show that: (1) Different types of bias correction methods have different correction focus and effects; their reflections on evaluation indicators are also different. (2) In the future period (2025–2050), the annual precipitation and average temperature in the Kaidu River Basin are higher than those in the historical period (1975–2014). The average annual temperature shows an upward trend in the future, but the annual precipitation shows a downward trend in the future except for the SSP2-4.5 scenario. (3) Compared with the historical period, the extreme precipitation in the future period under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios is higher than that in the historical period, and the number of rainless days decreases. In the future, under the SSP1-2.6 and SSP5-8.5 scenarios, the probability of meteorological drought events occurring due to high temperatures in the basin may further increase, while under the SSP2-4.5 scenario, the situation of high temperatures and heavy rain in the basin may continue to increase. [ABSTRACT FROM AUTHOR]

Wildfires are becoming more frequent due to the global climate change. Large amounts of greenhouse gases emitted by wildfires can lead to increases in extreme climate events. Accurately estimating the greenhouse gas carbon dioxide (CO2) emissions from wildfires is important for mitigation of climate change. In this paper, we develop a novel method to estimate wildfire CO2 emissions from the relationship between local CO2 emissions and XCO2 anomalies. Our method uses the WRF-Chem assimilation system from OCO-2 XCO2 retrievals which coupled with Data Assimilation Research Testbed (DART). To validate our results, we conducted three experiments evaluating the wildfire CO2 emissions over the conterminous United States. The four-month average wildfire emissions from July to October in 2015∼2018 were estimated at 4.408 Tg C, 1.784 Tg C, 1.514 Tg C and 2.873 Tg C, respectively. Compared to the average of established inventories CT2019B, FINNv1.5 and GFASv1.2 fire emissions, our estimates fall within one standard deviation, except for 2017 due to lacking of OCO-2 XCO2 retrievals. These results suggest that the regional carbon assimilation system, such as WRF-Chem/DART, using OCO-2 XCO2 retrievals has a great potential for accurately tracking regional wildfire emissions. [ABSTRACT FROM AUTHOR]

Nanotechnology, nanosensors in particular, has increasingly drawn researchers' attention in recent years since it has been shown to be a powerful tool for several fields like mining, robotics, medicine and agriculture amongst others. Challenges ahead, such as food availability, climate change and sustainability, have promoted such attention and pushed forward the use of nanosensors in agroindustry and environmental applications. However, issues with noise and confounding signals make the use of these tools a non-trivial technical challenge. Great advances in artificial intelligence, and more particularly machine learning, have provided new tools that have allowed researchers to improve the quality and functionality of nanosensor systems. This short review presents the latest work in the analysis of data from nanosensors using machine learning for agroenvironmental applications. It consists of an introduction to the topics of nanosensors and machine learning and the application of machine learning to the field of nanosensors. The rest of the paper consists of examples of the application of machine learning techniques to the utilisation of electrochemical, luminescent, SERS and colourimetric nanosensor classes. The final section consists of a short discussion and conclusion concerning the relevance of the material discussed in the review to the future of the agroenvironmental sector. [ABSTRACT FROM AUTHOR]

The present investigation elaborates on the level of game theory application for the applied and theoretical analyses of climate change and development studies. This editorial shows that the common good character of global climate alongside the increasing internalization of environmental externalities through national regulations and international environmental treaties jointly result in the increasing congruence between the context of climate change problems and the game theoretical method. Furthermore, the adoption of the Paris Accord by the overwhelming majority of developing countries as well as the disproportionate vulnerability of the Global South have led to an increasing shift in focus with regard to international development cooperation, from poverty alleviation and economic growth to green growth and circular economy solutions, within developing countries. The underutilization of game theory in the context of development studies is not satisfactory. This paper underlines the importance of implementing an impetus to researchers for scholarly discussions and applications of game theory in a discourse on the following topics: 1. economic growth; 2. climate change mitigation and adaptation; and 3. a broader socioeconomic development. [ABSTRACT FROM AUTHOR]

In this paper, we initiate a concept of graph-proximal functions. Furthermore, we give a notion of being generalized Geraghty dominating for a pair of mappings. This permits us to establish the existence of and unique results for a common best proximity point of complete metric space. Additionally, we give a concrete example and corollaries related to the main theorem. In particular, we apply our main results to the case of metric spaces equipped with a reflexive binary relation. Finally, we demonstrate the existence of a solution to boundary value problems of particular second-order differential equations. [ABSTRACT FROM AUTHOR]

The utilization of geological formations, distinguished by natural porosity and permeability and protected by low-permeability caprock, has emerged as an effective strategy for carbon dioxide (CO2) storage. This method significantly contributes to mitigating anthropogenic greenhouse gas emissions and addressing the challenges of climate change. Recent research has unveiled the potential of CO2 to enhance geothermal heat energy extraction in geothermal reservoirs by acting as a subsurface heat exchange fluid. This review paper explores the viability of CO2 in augmenting geothermal heat energy extraction, comparing it to conventional water-based geothermal systems. Special emphasis is placed on CO2 plume geothermal (CPG) systems, characterized by rapid deployment and long-term utilization of geothermal energy resources. With the overarching objective of establishing net-zero energy communities, the analysis of such systems offers a comprehensive understanding of their features, providing a fresh perspective on extracted energy within the context of energy supply in integrated, sustainable energy in built systems. Notably, these systems demonstrate efficacy in meeting the power requirements of an energy community, spanning both distinct heating and electricity needs. However, the key challenge lies in selecting suitable locations. This scientific review aims to comprehend the characteristics of CPG under specific temperature and pressure conditions while optimizing subsurface permeability. This insight is pivotal for identifying future locations for CPG operations with the intent of powering small energy communities. [ABSTRACT FROM AUTHOR]

Future climate change is expected to increase the risk and severity of wildland fires in tropical regions. Synoptic-scale fire weather conditions in Mexico were carefully analyzed using 20 years of satellite hotspot and rainfall data, hourly weather data, and various climate data. Fire analysis results showed that eighty-four percent of all fires in Mexico occurred south of 22° N. Southwest Mexico (SWM, N < 22°, 94–106° W) and Southeast Mexico (SEM, N < 22°, 86–94° W), account for 50% and 34% of all fires in Mexico. Synoptic-scale analysis results using hourly data showed that westerly wind sea breezes from the Pacific Ocean blow toward the coastal land areas of the SWM while easterly wind sea breezes from the Caribbean blow into the SEM. The most sensitive weather parameters were "relative humidity" for the SWM and "temperature" for the SEM. The fire-related indices selected were "precipitable water vapor anomaly" for the SWM and "temperature anomaly" for the SEM. The SWM fire index suggests that future fires will depend on dryness, while the SEM fire index suggests that future fires will depend on temperature trends. I do hope that this paper will improve local fire forecasts and help analyze future fire trends under global warming in Mexico. [ABSTRACT FROM AUTHOR]

In recent decades, climate change has become a critical global issue with far-reaching consequences for regional climates and ecosystems. While regional climate models provide valuable information, there is a growing need for high-resolution simulations to assess local impacts. This paper addresses this gap by presenting the first simulation of a 3 km convection-permitting (CP) scenario simulation for Bulgaria. The main aim of this study is to assess different precipitation indices and their future changes for Bulgaria under the Representative Concentration Pathway 8.5 (RCP8.5) scenario following the Coordinated Regional Climate Downscaling Experiment Flagship Pilot Study protocol. The simulations are evaluated against high-resolution observations. We downscale Coupled Model Intercomparison Project 5 Global Climate Model (CMIP5 GCM) data for historical (1995–2004) and future (2089–2098) periods using a regional climate model (RCM) at 15 km grid spacing and parametrized convection. We use these fields as initial and boundary conditions for convection-permitting kilometer-scale simulations. The 15 km grid spacing driving model is used as a reference to assess the added value of the kilometer-scale simulation. Additionally, the 3 km seasonal mean and projected 2 m temperature and the winter snow water equivalent are presented. The results show that the kilometer-scale simulation shows better performance of wet-hour intensity in all seasons, wet-hour frequency in the spring, fall, and winter, and extreme precipitation (99.9th percentile of all precipitation events, p99.9) in the winter and fall. The kilometer-scale simulation improves the projected precipitation distribution and modifies the signal of the precipitation frequency, intensity, and heavy precipitation change over some areas. A positive projected change in the wet-hour intensity is expected in all seasons (13.86% in spring, MAM, 17.48% in summer, JJA, 1.97% in fall, SON, and 17.43% in winter, DJF) and in the heavy precipitation in the spring (13.14%) and winter (31.19%) in the kilometer-scale experiment. The projected increase in mean winter precipitation is accompanied by a significant decrease in mean winter snowfall over lowlands (50−70%). The convection-permitting Regional Climate Model, version 4.7.1 (RegCM4.7.1) suggests an increase in winter snowfall over the highest parts of the country, but a significant increase in the 2 m temperatures there. The results of this study are encouraging and may be of interest to the community of climate scientists and users of climate data for making reliable estimates of the local impacts of future climate change. [ABSTRACT FROM AUTHOR]

Natural gas is considered a transitional energy source in the transition to clean energy owing to its clean, efficient, and ecologically beneficial properties. The trade of liquefied natural gas (LNG) serves as the backbone of the global natural gas trade and significantly influences the dynamics of the global energy trade system. This paper constructs long-term global LNG trade networks, and explores the spatiotemporal evolution and topological structures of the international LNG trade by utilizing multilevel network analysis methods, to provide insights for comprehensively understanding the market dynamics of the global LNG trade system. The findings indicate the following: (1) The global LNG trade volume shows an overall upward trend, and the global LNG trade network exhibits uneven spatial distribution, clear hierarchical differentiation, and an increasingly complicated structure. Global LNG trade is gradually changing from regionalization to globalization, and the international LNG market is undergoing structural reshaping. (2) The global LNG trade network continues to expand in size and density, and the rapidly growing LNG supply and trade relations are driving the formation of the global natural gas market. (3) Global LNG trade is still in a phase of rapid change, with the global efficiency of the network increasing and then decreasing. The trade network has traditionally been centered on ten countries, including Japan, South Korea, the United States, and Qatar. (4) The global LNG trade network exhibits clear core-periphery structures with considerable polarization effects, and the trade network structure is continuously evolving and is growing unbalanced. Finally, we put forward relevant policy suggestions to promote global LNG trade interconnectivity and enhance environmental protection and respond to global climate change. [ABSTRACT FROM AUTHOR]

Simple Summary: Diseases pose a significant challenge for aquaculture, exacerbated by changing weather conditions. The sector has explored various strategies, including maintaining a clean environment and employing vaccines, to combat these diseases. However, these solutions are effective only against specific diseases and species. In our recent research, we investigated the use of genetics to enhance disease resistance in three crucial species: white leg shrimp, striped catfish, and yellowtail kingfish. Our findings indicate that the studied populations of these species possess genes that can be inherited, imparting greater resistance to diseases such as White Spot Syndrome Virus, Bacterial Necrotic Pancreatitis, and skin fluke. By selectively breeding animals with these resistant genes, we successfully increased resistance within the population, promoting overall fish health and boosting production. Additionally, we examined these genes and utilized computer models to predict the most resistant individuals for breeding to combat diseases. Looking ahead, our focus is on omics technologies, precision farming systems, and advanced algorithms to further enhance the disease resistance of these species, thereby making aquaculture more sustainable and resilient against threats. Diseases pose a significant and pressing concern for the sustainable development of the aquaculture sector, particularly as their impact continues to grow due to climatic shifts such as rising water temperatures. While various approaches, ranging from biosecurity measures to vaccines, have been devised to combat infectious diseases, their efficacy is disease and species specific and contingent upon a multitude of factors. The fields of genetics and genomics offer effective tools to control and prevent disease outbreaks in aquatic animal species. In this study, we present the key findings from our recent research, focusing on the genetic resistance to three specific diseases: White Spot Syndrome Virus (WSSV) in white shrimp, Bacterial Necrotic Pancreatitis (BNP) in striped catfish, and skin fluke (a parasitic ailment) in yellowtail kingfish. Our investigations reveal that all three species possess substantial heritable genetic components for disease-resistant traits, indicating their potential responsiveness to artificial selection in genetic improvement programs tailored to combat these diseases. Also, we observed a high genetic association between disease traits and survival rates. Through selective breeding aimed at enhancing resistance to these pathogens, we achieved substantial genetic gains, averaging 10% per generation. These selection programs also contributed positively to the overall production performance and productivity of these species. Although the effects of selection on immunological traits or immune responses were not significant in white shrimp, they yielded favorable results in striped catfish. Furthermore, our genomic analyses, including shallow genome sequencing of pedigreed populations, enriched our understanding of the genomic architecture underlying disease resistance traits. These traits are primarily governed by a polygenic nature, with numerous genes or genetic variants, each with small effects. Leveraging a range of advanced statistical methods, from mixed models to machine and deep learning, we developed prediction models that demonstrated moderate-to-high levels of accuracy in forecasting these disease-related traits. In addition to genomics, our RNA-seq experiments identified several genes that undergo upregulation in response to infection or viral loads within the populations. Preliminary microbiome data, while offering limited predictive accuracy for disease traits in one of our studied species, underscore the potential for combining such data with genome sequence information to enhance predictive power for disease traits in our populations. Lastly, this paper briefly discusses the roles of precision agriculture systems and AI algorithms and outlines the path for future research to expedite the development of disease-resistant genetic lines tailored to our target species. In conclusion, our study underscores the critical role of genetics and genomics in fortifying the aquaculture sector against the threats posed by diseases, paving the way for more sustainable and resilient aquaculture development. [ABSTRACT FROM AUTHOR]

In recent years, global climate change causes more extreme precipitation events in Lancang-Mekong River Basin (Lancang-Mekong Basin). It leads to the increase of rainstorm and flood risk, which poses a threat to the flood control safety of Lancang-Mekong Basin. Based on the precipitation data of four global climate models of CMIP6, this paper selected six indexes, including PRCPTOT and R10, to study the change characteristics of the extreme precipitation indexes in the Lancang-Mekong Basin during 1980–2020 and predicted the development trend of the extreme precipitation indexes in the Lancang-Mekong Basin during 2021–2050. The results show that from 1980 to 2020, the extreme precipitation events in regions I and IV showed an increasing trend, while those in regions II and III showed a decreasing trend. From 2021 to 2050, the extreme precipitation events in all regions of the Lancang-Mekong River show an upward trend, and most indexes increased compared with the historical period. The extreme precipitation indexes in most regions increases the most under the scenario of SSP5-8.5, while the increase is small under the scenario of SSP2-4.5; The six extreme precipitation indexes selected in the paper can better reflect the trend of extreme precipitation events and floods. The trend of extreme precipitation indexes in the Lancang-Mekong River basin in the future is more significant, which indicates that it may be affected more frequently by extreme precipitation events and floods. [ABSTRACT FROM AUTHOR]

This paper explores the role of electricity in the transition to renewable energy to mitigate climate change. A systematic literature review with the Scopus database identified 92 papers relevant to the renewable electricity transition. A PESTLE (Political, Economic, Sociological, Technological, Legal, and Environmental) review of the papers provided a multidisciplinary analysis. The Paris Agreement created a global movement for carbon neutrality to address the threats of climate change, calling for a transition to renewable electricity to lead the way and expand into new sectors and regions. Although smaller renewable technologies are ramping up, complexities thwarting the transition include locked-in assets, high upfront costs, variability of solar and wind energy, infrastructure, difficulty in decarbonizing transportation and industry, material resource constraints, and fossil fuel support. This research found that renewables are not replacing fossil fuels to date but adding further energy demands, so that greenhouse gas emissions rose in 2021 despite an increased renewable electricity share. Without a major shift in the trillions of dollars of subsidies and investment away from fossil fuels to renewables, catastrophic climate change is predicted. This paper found that the Paris Agreement's commitment to net-zero carbon and the transition to renewable electricity are undermined by record-high levels of subsidies and financing for fossil fuel industry expansion. Transitioning to a climate-neutral economy requires an investment away from fossil fuels into renewable energy ecosystems. Renewable electricity provides possibilities to realize sustainable development goals, climate stabilization, job creation, a green economy, and energy security with careful planning. [ABSTRACT FROM AUTHOR]

In response to the pressing global challenges around climate change and the imperative of transitioning the energy market towards sustainability, this paper presents a comprehensive review starting from the late 18th century. The study examines the pivotal role of Transmission System Operators (TSOs) and Distribution System Operators (DSOs) in shaping the evolving energy landscape, with a specific emphasis on the C+++ Framework. This framework emphasizes coordination, cooperation, and collaboration between TSOs and DSOs to achieve sustainable energy systems through the integration of renewable energy technologies, storage systems, and efficient energy demand management. In addition, the review provides a historical overview of global warming from 1800 to the present, highlighting key events and developments related to greenhouse gas emissions. Furthermore, the paper delves into the significance of international agreements such as the Paris Agreement and the importance of reducing greenhouse gas emissions for a sustainable future. Recognizing the vital role of the C+++ Framework, the paper concludes with a discussion of future hybrid sustainable technologies incorporating various storage and efficient lighting technologies that can optimize energy management and reduce carbon emissions. This research aims to contribute valuable insights to inform energy policy and decision-making processes for a reliable, efficient, and sustainable energy delivery system. [ABSTRACT FROM AUTHOR]

This paper deals with the analysis of the temperatures in a group of 36 African countries. By looking at the maximum, minimum and the range (the difference between the maximum and the minimum) and using a long memory model based on fractional integration and cointegration, we first show that all series display a long memory pattern, with a significant positive time trend in 29 countries for the maximum temperatures and in 33 for the minimum ones. Looking at the range, the estimated value for the order of integration is smaller than the one based on maximum or minimum temperatures in 17 countries. Performing fractional cointegration tests between the maximum and minimum temperatures, our results indicate that the two series cointegrate in the classical sense (i.e., with a short memory equilibrium relationship) in a group of 11 countries, and there is another group of eight countries displaying cointegration in the fractional sense. The remaining 17 countries with no evidence of cointegration are therefore at a very high risk of climate change due to the absence of long-term co-movement in their maximum and minimum temperatures. Findings in this paper are of tremendous interpretations and relevance for the analysis and climate projections in Africa. [ABSTRACT FROM AUTHOR]

This paper compares the ideas contained in the main papers published on climate change since World War II to arrive at a suggested consensus of our present knowledge regarding climatic changes and their causes. Atmospheric carbon dioxide is only suggested as a cause in one theory, which, despite its wide acceptance by Politicians, the media, and the Public, ignores the findings in other studies, including the ideas found in the Milankovitch Cycles. It also does not explain the well-known NASA map of the changes between the global 1951–1978 and the 2010–2019 mean annual temperatures. The other theories by Oceanographers, Earth scientists, and Geographers fit together to indicate that the variations in climate are the result of differential solar heating of the Earth, resulting in a series of processes redistributing the heat to produce a more uniform range of climates around the surface of the Earth. Key factors are the shape of the Earth and the Milankovitch Cycles, the distribution of land and water bodies, the differences between heating land and water, ocean currents and gateways, air masses, and hurricanes. Low atmospheric carbon dioxide levels during cold events could result in too little of this gas to support photosynthesis in plants, resulting in the extermination of most life on Earth as we know it. The 23 ka Milankovitch cycle has begun to reduce the winter insolation received at the surface of the atmosphere in the mid-latitudes of the Northern Hemisphere starting in 2020. This results in extreme weather as the winter insolation reaching the surface of the atmosphere in the higher latitudes of the Northern Hemisphere decreases while the summer air temperatures increase. It heralds the start of the next glaciation. A brief outline is given of some of the climatic changes and consequences that may be expected in western Canada during the next 11.5 ka. [ABSTRACT FROM AUTHOR]

Energy efficiency is, in principle, a simple idea: an output of human value, for example, vehicle-km traveled, divided by the needed input energy. Efficiency improvements are regarded as an important means of mitigating not only climate change, but also other environmental problems. Despite the vast number of articles published on energy efficiency, a few people question whether it is a useful or accurate measure in its present form; nearly all papers are either engineering studies, or address barriers to efficiency improvements. This review addresses this issue via a critical review of the literature, including not only papers on energy efficiency, but those on adjacent areas of research that can help broaden the scope, both geographically and conceptually. These shortcomings are illustrated in case studies of buildings/cities and road passenger transport. The main findings of this review are that (1) energy efficiency inevitably has an ethical dimension, as well as a technical one, in that feedbacks are more widespread than they have generally considered to be, and (2) that conventional efficiency measures omit important energy input items, particularly those concerned with the mining the materials needed for renewable energy plants. The key conclusions are that present efficiency measures are not adequate, and future research is needed to overcome these shortcomings. [ABSTRACT FROM AUTHOR]

As we confront novel environmental challenges, a full understanding of the physical and biological processes that govern species responses to climate change will help maintain biodiversity and support conservation measures that are more robust to irreducible uncertainty. However, climate impacts are so complex, and the literature on salmon and trout is so vast that researchers and decision makers scramble to make sense of it all. Therefore, we conducted a systematic literature review of climate impacts on salmon and anadromous trout as a resource for stakeholders, managers, and researchers. We reviewed studies published from 2010 to 2021 that address climate impacts on these fish and organized them in a database of 1169 physical and 1853 biological papers. Papers are labeled with keywords across eight categories related to subject matter and study methods. We compared the literature by biological process and life stage and used these comparisons to assess strengths and weaknesses. We then summarized expected phenotypic and genetic responses and management actions by life stage. Overall, we found the largest research gaps related to species interactions, behavioral responses, and effects that carry over across life stages. With this collection of the literature, we can better apply scarce conservation resources, fill knowledge gaps, and make informed decisions that do not ignore uncertainty. [ABSTRACT FROM AUTHOR]

Bridging the Pacific provides an image of two separate regions which share the Pacific Rim, becoming connected in multiple aspects, not limited to a predominantly economic interconnection. This image, thus, points toward a constructive mechanism to work toward a resolution of the over-arching global issue, including climate change. This image and the method for its practical implementation are suggested by the Forum for East Asia and Latin America Cooperation—FEALAC. In the overall field of regionalism studies, inter-regionalism can be considered as 'the third wave' under the new regionalism, and FEALAC can be located in this analytical framework. To begin, this paper traces the process and features of regionalism by typification of three different versions, or ideal types of regionalism. In this work, each type of regionalism is not viewed as a successor to, considered better than the previous one, but rather provides a typology for analytical purposes. Next, taking up this historical and analytical framework of regionalism, this paper examines the FEALAC in terms of its strengths and weaknesses considered from the perspective of the five systemic functions. Finally, this paper demonstrates the ways in which FEALAC can be considered a relevant framework of inter-regional governance. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have become a viable solution to the emerging global climate crisis. Rechargeable battery packs are the basic unit of the energy storage system of these vehicles. The battery thermal management system (BTMS) is the primary control unit of the energy source of the vehicles. EV performance is governed by specific power, charging/discharging rate, specific energy, and cycle life of the battery packs. Nevertheless, these parameters are affected by temperature, making thermal management the most significant factor for the performance of a battery pack in an EV. Although the BTMS has acquired plenty of attention, research on the efficiency of the liquid cooling-based BTMS for actual drive cycles has been minimal. Liquid cooling, with appropriate configuration, can provide up to 3500 times more efficient cooling than air cooling. Direct/immersive and indirect liquid cooling are the main types of liquid cooling systems. Immersive/direct cooling utilizes the technique of direct contact between coolant and battery surface, which could provide larger heat transfer across the pack; however, parameters such as leakage, configuration, efficiency, etc., are needed to be considered. Indirect cooling techniques include cold plates, liquid jackets, discrete tubes, etc. It could result in complex configuration or thermal non-uniformity inside the pack. The paper intends to contribute to the alleviation of these gaps by studying various techniques, including different configurations, coolant flow, nanoparticles, varying discharging rates, different coolants, etc. This paper provides a comprehensive perspective of various techniques employed in liquid cooling battery packs, identifying the shortcomings in direct/immersive and indirect liquid cooling systems and discussing their mitigation strategies. [ABSTRACT FROM AUTHOR]

The current educational climate poses challenges for both the educator and learner when it comes to deep learning. A significant part of the solution lies in providing both time and space wherein a deep learning can occur, despite the challenges. After situating the SALT Approach (acronym for Scripture and Liturgy Teaching Approach), progressing from its doctoral study roots, the paper unpacks ways that the SALT approach harnesses time to facilitate deep thinking. The pedagogical ways discussed can also be adapted for use across the curriculum, as the 'toolbox' of ideas in the final section of the paper summarizes elements that can facilitate a classroom climate and setting leading to peace and productivity in the learning space. [ABSTRACT FROM AUTHOR]

Climate change has led to a variety of disasters that have caused damage to infrastructure and the economy with societal impacts to human living. Understanding people's emotions and stressors during disaster times will enable preparation strategies for mitigating further consequences. In this paper, we mine emotions and stressors encountered by people and shared on Twitter during Hurricane Harvey in 2017 as a showcase. In this work, we acquired a dataset of tweets from Twitter on Hurricane Harvey from 20 August 2017 to 30 August 2017. The dataset consists of around 400,000 tweets and is available on Kaggle. Next, a BERT-based model is employed to predict emotions associated with tweets posted by users. Then, natural language processing (NLP) techniques are utilized on negative-emotion tweets to explore the trends and prevalence of the topics discussed during the disaster event. Using Latent Dirichlet Allocation (LDA) topic modeling, we identified themes, enabling us to manually extract stressors termed as climate-change-related stressors. Results show that 20 climate-change-related stressors were extracted and that emotions peaked during the deadliest phase of the disaster. This indicates that tracking emotions may be a useful approach for studying environmentally determined well-being outcomes in light of understanding climate change impacts. [ABSTRACT FROM AUTHOR]

Capturing and storing CO2 (CCS) was once regarded as a significant, urgent, and necessary option for reducing the emissions of CO2 from coal and oil and gas industries and mitigating the serious impacts of CO2 on the atmosphere and the environment. This recognition came about as a result of extensive research conducted in the past. The CCS cycle comes to a close with the last phase of CO2 storage, which is accomplished primarily by the adsorption of CO2 in the ocean and injection of CO2 subsurface reservoir formation, in addition to the formation of limestone via the process of CO2 reactivity with reservoir formation minerals through injectivities. CCS is the last stage in the carbon capture and storage (CCS) cycle and is accomplished chiefly via oceanic and subterranean geological sequestration, as well as mineral carbonation. The injection of supercritical CO2 into geological formations disrupts the sub-surface's existing physical and chemical conditions; changes can occur in the pore fluid pressure, temperature state, chemical reactivity, and stress distribution of the reservoir rock. This paper aims at advancing our current knowledge in CO2 injection and storage systems, particularly CO2 storage methods and the challenges encountered during the implementation of each method and analyses on how key uncertainties in CCS can be reduced. CCS sites are essentially unified systems; yet, given the scientific context, these storage systems are typically split during scientific investigations based on the physics and spatial scales involved. Separating the physics by using the chosen system as a boundary condition is a strategy that works effectively for a wide variety of physical applications. Unfortunately, the separation technique does not accurately capture the behaviour of the larger important system in the case of water and gas flow in porous media. This is due to the complexity of geological subsurface systems, which prevents the approach from being able to effectively capture the behaviour of the larger relevant system. This consequently gives rise to different CCS technology with different applications, costs and social and environmental impacts. The findings of this study can help improve the ability to select a suitable CCS application method and can further improve the efficiency of greenhouse gas emissions and their environmental impact, promoting the process sustainability and helping to tackle some of the most important issues that human being is currently accounting global climate change. Though this technology has already had large-scale development for the last decade, some issues and uncertainties are identified. Special attention was focused on the basic findings achieved in CO2 storage operational projects to date. The study has demonstrated that though a number of CCS technologies have been researched and implemented to date, choosing a suitable and acceptable CCS technology is still daunting in terms of its technological application, cost effectiveness and socio-environmental acceptance. [ABSTRACT FROM AUTHOR]

Accurate prediction of storm surges is crucial for mitigating the impact of extreme weather events. This paper introduces the Bidirectional Attention-based Long Short-Term Memory (LSTM) Storm Surge Architecture, BALSSA, addressing limitations in traditional physical models. By leveraging machine learning techniques and extensive historical and real-time data, BALSSA significantly enhances prediction accuracy. Utilizing a bidirectional attention-based LSTM framework, it captures complex, non-linear relationships and long-term dependencies, improving the accuracy of storm surge predictions. The enhanced model, D-BALSSA, further amplifies predictive capability through a doubled bidirectional attention-based structure. Training and evaluation involve a comprehensive dataset from over 70 typhoon incidents in Macao between 2017 and 2022. The results showcase the outstanding performance of BALSSA, delivering highly accurate storm surge forecasts with a lead time of up to 72 h. Notably, the model exhibits a low Mean Absolute Error (MAE) of 0.0287 m and Root Mean Squared Error (RMSE) of 0.0357 m, crucial indicators measuring the accuracy of storm surge predictions in water level anomalies. These metrics comprehensively evaluate the model's accuracy within the specified timeframe, enabling timely evacuation and early warnings for effective disaster mitigation. An adaptive system, integrating real-time alerts, tropical cyclone (TC) chaser, and prospective visualizations of meteorological and tidal measurements, enhances BALSSA's capabilities for improved storm surge prediction. Positioned as a comprehensive tool for risk management, BALSSA supports decision makers, civil protection agencies, and governments involved in disaster preparedness and response. By leveraging advanced machine learning techniques and extensive data, BALSSA enables precise and timely predictions, empowering coastal communities to proactively prepare and respond to extreme weather events. This enhanced accuracy strengthens the resilience of coastal communities and protects lives and infrastructure from the escalating threats of climate change. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to assess pro-environmental behavior, climate change anxiety, perception, hope, and despair in different political orientations. Our specific aims included to assess the validity of all the instruments used; to assess whether the factor structure of the scales were valid across political orientations; to evaluate their reliability; to assess differences concerning age, gender, and political orientation; to learn the variables that explain pro-environmental behavior; and to evaluate the moderating role of climate change perception, despair, and hope in the relationship between climate change anxiety and pro-environmental behavior. Confirmatory factor analyses (CFAs), multi-group CFAs (to calculate measurement invariance), multiple linear regressions, and moderations were performed. Results showed that pro-environmental behavior and climate change hope achieved the four assessed levels of invariance across different political orientations; climate change anxiety achieved the first three levels of invariance; and climate change perception and climate change despair achieved configural invariance. Climate change anxiety, personal experience with climate change, and climate change perception (total, reality, and consequences) presented higher values for the left political orientation than for the right or the center. Climate change anxiety variables contributed most to explaining pro-environmental behaviors. Hope, despair, and climate change perception (consequences) moderated the relationship between climate change anxiety and pro-environmental behavior. These results open up new avenues for investigation, specifically to understand why high levels of anxiety lead to more pro-environmental behaviors. [ABSTRACT FROM AUTHOR]

Understanding the survival needs of neglected or underutilized species (NUS) in agroforestry can offer valuable insights to mitigate climate change and biodiversity loss. This paper analyses the habitat heterogeneity of Buxus balearica populations (palaeorelict of the Mediterranean Basin) using a methodology that integrates four approaches: ecological profiling, multivariate analysis, and the clustering of populations according to environmental variables and suitability. The habitat analysis was conducted on 240 occurrence points, integrating open-source databases and germplasm collection field work. Results reveal that the distribution of B. balearica in the south of the Iberian Peninsula is mainly conditioned by thermal variations, the humidity regime, and microtopography, which makes it very vulnerable to current and future climate change scenarios. The analysis of habitat heterogeneity demonstrates its capacity to estimate genetic diversity, which provides a solid basis for future research and conservation actions. The rapid extinction process of these boxwoods is evident by comparing the suitability model under different climate scenarios. There is an urgent need to improve the current status of the species and associated landscapes, considering B. balearica as an underutilized species, and establishing a sound management plan to reinforce its populations and protect its natural habitat. [ABSTRACT FROM AUTHOR]

Simple Summary: In this paper, the differences in carbon sequestration microbial communities in different wetland types and their main influencing factors are investigated. It was found that Proteobacteria was the dominant group of carbon sequestration microorganisms in the marsh wetland and river source wetland, and Cyanobacteria was the dominant group of carbon sequestration microorganisms in the lakeside wetland. The α diversity, relative abundance of Proteobacteria and total carbon content of the marsh wetland were the highest, followed by the river source wetland and lakeside wetland. The soil temperature and moisture and the total carbon content were the most important factors affecting the structures of carbon-sequestering microbial communities. Compared with the riverhead and lakeside wetlands, the marsh wetland has the highest carbon storage. The soil carbon storage in the Qinghai–Tibet Plateau wetlands is affected by microbiota and wetland types, but the response mechanisms of carbon sequestration microorganisms on the Qinghai–Tibet Plateau to different wetland types are still poorly described. To explore the differences in carbon sequestration microbial communities in different wetlands and the main influencing factors, this study took a marsh wetland, river source wetland and lakeside wetland of Qinghai Lake as the research objects and used high-throughput sequencing to study the functional gene, cbbL, of carbon sequestration microorganisms. The results showed that the dominant bacterial group of carbon sequestration microorganisms in marsh and river source wetlands was Proteobacteria, and the dominant bacterial group in the lakeside wetland was Cyanobacteria. The alpha diversity, relative abundance of Proteobacteria and total carbon content were the highest in the marsh wetland, followed by the river source wetland, and they were the lowest in the lakeside wetland. In addition, the physical and chemical characteristics of the three wetland types were significantly different, and the soil temperature and moisture and total carbon content were the most important factors affecting the community structures of carbon-sequestering microorganisms. There was little difference in the total nitrogen contents between the marsh wetland and river source wetland. However, the total nitrogen content was also an important factor affecting the diversity of the carbon sequestration microbial community. In summary, the wetland type significantly affects the process of soil carbon sequestration. Compared with the riverhead and lakeside wetlands, the marsh wetland has the highest carbon storage. [ABSTRACT FROM AUTHOR]

The occurrence of late spring frosts due to climate change causes great damage to plantation production worldwide. The main objective of the paper is to provide a comprehensive overview of the problem and to outline effective protective measures against late spring frosts. The nature of frost depends on regional, altitudinal, and geographic differences, but they all share a common problem: they remove heat, resulting in the freezing of new plant growth and flowers. Tissue freezing is affected by critical temperatures and the frost type, intensity, and duration. Protection against late spring frosts can be broadly divided into three categories: active, passive, and chemical measures. In the field of agricultural engineering, various techniques have been thoroughly researched, and their effectiveness has been confirmed by research. These include various sprinkler systems, different heating devices, and large-diameter fans. Conclusive findings are being made on the performance of these systems in sub-zero temperatures and their cost-effectiveness. Climate change increases the importance of protecting permanent crops from late spring frosts and requires advances in agricultural technology to meet changing production demands and challenges. [ABSTRACT FROM AUTHOR]

We present a floristic–vegetational study on a plant community dominated by Geranium argenteum in the Sibillini Mountains (Central Apennines), at the southern limit of its distribution in the Apennines. It is a rare pioneer community located at an elevation of about 2100 m a.s.l. with northern exposure on the fractured rocky ridges and at the edges of the rocky detrital valleys on lithosol, with a prolonged presence of snowpack and gelifraction processes. The results of the phytosociological analysis allow us to propose the new Festuco italicae-Geranietum argentei association referred to as the Leontopodio nivalis-Elynion myosuroidis alliance (Carici rupestris-Kobresietea bellardii class). The comparison with the Alpine and the Northern Apennines phytocoenoses characterized by Geranium argenteum allows us to provide a new interpretation of the syntaxonomical framework concerning the Geranium argenteum communities within its Alpine–Apennine range in light of the new data presented in this paper. The new Festuco italicae-Geranietum argentei association represents a further contribution to the knowledge of the relict alpine vegetation of the Leontopodio nivalis-Elynion myosuroidis alliance in the Sibillini Mountains and thus in the Central Apennines. Finally, habitat monitoring will be essential for assessing the impacts of climate change on this fragile and narrowly restricted plant community. [ABSTRACT FROM AUTHOR]

With climate change, more intense weather phenomena can be expected, including pressure drops related to the arrival of an atmospheric front. Such drops of pressure are the main reason for gas emissions from closed mines to the surface, and a closed, empty mine shaft is the most likely route of this emission. Among the gases emitted, the most important are carbon dioxide and methane, creating a twofold problem—greenhouse gas emissions and gas hazards. The work presented in this paper simulated the spread of the mentioned gases near such an abandoned shaft for four variants: model validation, the most dangerous situations found during measurements with or without wind, and a forecast variant for a possible future pressure drop. It was found that a momentary CO2 emission of 0.69 m3/s and a momentary CH4 emission of 0.29 m3/s are possible, which for one hour of the appropriate drop would give hypothetically 2484 m3 CO2 and 1044 m3 CH4. In terms of gas hazards, the area that should be monitored and protected may exceed 25 m from a closed shaft in the absence of wind influence. The wind spreads the emitted gases to distances exceeding 50 m but dilutes them significantly. [ABSTRACT FROM AUTHOR]

Since the droughts of the 1970s–1980s, populations in the Sahel region have opted for a mass exodus to more humid urban or rural centers. Migrations or exoduses have accelerated in recent decades due to environmental degradation and unfavorable climatic conditions. Insufficient harvests are the main reason for migration for the majority of migrants in the Sahelian areas. Migration is a major adaptation strategy to cope with extreme climatic conditions, thus requiring quantification in the destination area. The aim of this paper is to propose a metapopulation model to approximate reality by identifying the transition from one socioeconomic vulnerability group to another, from a less favorable area to favorable area in terms of natural resources, depending on the strategies, policies, and climate variability. The model was used to analyze the dynamics of socioeconomic vulnerability to study the impact of migration on the dynamics of socioeconomic vulnerability. The developed mathematical model was analyzed. Up to 2050, simulations applied to the Tougou village in northern Burkina Faso show that migration has a positive impact on the socioeconomic vulnerability of the destination area, thereby reducing the vulnerability of the population by 10% when resources are increased by up to 30%. [ABSTRACT FROM AUTHOR]

Climate change indices (CCI) profoundly contribute to understanding the climate and its change. They are used to present climate change in an easy-to-understand and tangible way, thus, facilitating climate communication. Most of these indices are calculated by daily data but there are also many valuable data sets that consist solely of a monthly temporal frequency. In this paper, we present a method that enables the estimation of specific CCIs from monthly instead of daily data, allowing the expression and examination of data sets consisting solely of monthly parameters through climate change indices. Therefore, we used the ERA5 Land data supplemented by a CMIP6 ssp5-8.5 climate projection to train multiple regression models with different regression functions and selected the best fitting for every grid point. Using a climate projection as a supplement in training the regression functions accounts for climate change and empowers the method's application in future climate periods. The method includes a simple bias adjustment (delta change). Its output is regridded to ERA5 Land's 0. 1 ∘ grid, adapting it to the local environment and offering better application in areas with complex terrain using coarse data. Furthermore, the presented method and its regression coefficients can be created from any data set, allowing an even higher spatial resolution than ERA5 Land's. While the method performs best for the temperature-related indices in warm temperate climates, it works uniformly well for the precipitation-related index maximum consecutive dry days on a global scale. [ABSTRACT FROM AUTHOR]

Climate change mitigation is one of the most important challenges facing the modern world. It is necessary to monitor the development of new concepts and technologies and take a stab at identifying disruptive innovations, which have the potential of becoming real climate-friendly game changers. The aim of this paper is to examine the patterns of inventive activity aimed at mitigating climate change in the maritime industry with respect to other transport modes. Appropriate research tools in the area of patent analysis were selected and utilised. A new class of patents related to climate change in maritime transport (CPC-Y02T70/00) was used as a data source. The original value of the study consists of offering a complete picture of the efforts made in patenting activity in climate change mitigation in the maritime transport, with a look at leading applicants and countries, knowledge flows, the most robustly developed and underdeveloped technical fields. A map of technical knowledge flows for climate change mitigation in transport was constructed. The research results show that inventions for the maritime industry are less hermetic than those for air and road transport; however, they are not as much linked with previously developed solutions. The most intensively developed technical fields include the design and construction of watercraft hulls (1) and measures to reduce greenhouse gas emissions related to the propulsion system (2). Among the technologies whose further development merits close attention are solutions related to electrical propulsion and wave energy. At the same time, inventive activity in the area of climate change adaptation dedicated to ports is insignificant and definitely needs more support from the community of scientists and inventors. Building knowledge based on patent information can help universities, research institutions, shipyards, manufacturers of marine equipment and other business entities to identify the technologies of the greatest potential for further development. [ABSTRACT FROM AUTHOR]

The challenges associated with climate change are increasing, so there is an urgent need for modern tools to effectively assess, predict and minimise climate risks. This research paper presents the results of the development of the innovative Community Climate Change Impacts Service (C3IS) system, which represents a paradigm shift in climate risk analysis. C3IS is a module that includes a pioneering set of tools with an interactive application programming interface (API) fully integrated with the Google Earth Engine (GEE) platform. The C3IS module enables lightning-fast collection and visualisation (in real time) of critical climate risk data through flexible integration with GEE. The advantages of this integration are the ability to use the GEE platform to access an extensive petabyte-scale catalogue of geospatial data and an ever-expanding database of satellite imagery. The defining feature of the developed module is accessibility and usability due to the exclusion of operations such as the time-consuming preliminary processing of "big data"; complex modelling; and large-scale data storage. The study shows the promising application of the C3IS module for the operational decision making and development of sound strategies for effective climate change mitigation. [ABSTRACT FROM AUTHOR]

With the ongoing changes in global climate, ocean data play a crucial role in understanding the complex variations in the earth system. These variations pose significant challenges to human efforts in addressing the changes. As a data hub for the satellite geodetic technique, unmanned aerial vehicles (UAVs) instill new vitality into ocean data collection due to their flexibility and mobility. At the same time, the dual-functional radar-communication (DFRC) system is considered a promising technology to empower ubiquitous communication and high-accuracy localization. In this paper, we explore a new fusion of UAV and DFRC to assist data acquisition in the ocean surveillance scenario. The floating buoys transmit uplink data transmission to the UAV with non-orthogonal multiple access (NOMA) and attempt to localize the target cooperatively. With the mobility of the UAV and power control at the buoys, the system throughput and the target localization performance can be improved simultaneously. To balance the communication and sensing performance, a two-objective optimization problem is formulated by jointly optimizing the UAV's location and buoy's transmit power to maximize the system throughput and minimize the attainable localization mean-square error. We propose a joint communication and radar-sensing many-objective optimization (CRMOP) algorithm to meliorate the communication and radar-sensing performance simultaneously. Simulation results demonstrate that compared with the baseline, the proposed algorithm achieves superior performance in balancing the system throughput and target localization. [ABSTRACT FROM AUTHOR]

International airports are responding to the threat of climate change and various man-made hazards by proposing impact protection measures. Airport managers and risk controllers should develop a comprehensive risk assessment model to measure the mutual influence relationships of resilience factors. In this paper, the problem of treating resilience factors as independent ones in previous studies is overcome. In this study, we not only develop a framework for assessing resilience factors in international airports based on an aviation safety perspective, but also develop the Fermatean fuzzy decision-making trial and evaluation laboratory (FF-DEMATEL) to identify the mutual influence relationships of resilience factors. Fermatean fuzzy sets are incorporated in DEMATEL to reflect information incompleteness and uncertainty. The critical resilience factors of international airports were identified through real-case analysis. In terms of importance, the results show that rescue capability is a core capability that is important for airport resilience. In addition, "security management system (SeMS) integrity", "education and training of ground staff on airport safety awareness", "first aid mechanism for the injured", and "adequate maintenance equipment for rapid restoration tasks" are identified as key factors that are given more weights. On the other hand, in terms of influence strength, the detection capability has the highest total influence and significantly influenced the other resilience capabilities. Finally, the influential network relation map (INRM) is utilized to assist decisionmakers in swiftly comprehending the impact of factors and formulating viable strategies to enhance airport resilience. This enables airport managers and risk controllers to make informed decisions and allocate resources efficiently. [ABSTRACT FROM AUTHOR]

Confronted with the climate crisis, the world is making tremendous efforts in energy transition, such as expanding renewable energy that does not emit carbon. The importance of virtual power plant (VPP) operation technology has emerged to secure grid flexibility in response to the expanding renewable energy implemented due to these efforts. Accordingly, VPPs, which include photovoltaics, wind turbines, heating, ventilation, and air conditioning (HVAC), load, and EV, have been constructed. HVAC, one of the component resources, is a system that controls and regulates temperature, humidity, and airflow. Since it responds sensitively to the building's heat capacity and changes in the external environment, it requires continuous and stable control. In this paper, we used data-based modeling to implement the HVAC required for the optimal operation of VPP. Since accurately creating an equation-based HVAC model was difficult considering building information modeling and external environment variables, we used historical HVAC operation data to perform data-based modeling. The model was implemented using nonlinear regression and machine learning, such as a support vector machine and artificial neural network. Then, the data-based HVAC and the actual HVAC operation results were comparatively analyzed based on a case study, and the model's goodness-of-fit was evaluated based on performance metrics. Model performance indicators confirmed that the ANN-based HVAC model was most similar to the actual HVAC system. [ABSTRACT FROM AUTHOR]

Recent research has revealed that the dynamics of autumn phenology play a decisive role in the inter-annual changes in the carbon cycle. However, to date, the shifts in autumn phenology (EGS) and the elements that govern it have not garnered unanimous acknowledgment. This paper focuses on the Yellow River Basin (YRB) ecosystem and systematically analyzes the dynamic characteristics of EGS and its multiple controls across the entire region and biomes from 1982 to 2015 based on the long-term GIMMS NDVI3g dataset. The results demonstrated that a trend toward a significant delay in EGS (p < 0.05) was detected and this delay was consistently observed across all biomes. By using the geographical detector model, the association between EGS and several main driving factors was quantified. The spring phenology (SGS) had the largest explanatory power among the interannual variations of EGS across the YRB, followed by preseason temperature. For different vegetation types, SGS and preseason precipitation were the dominant driving factors for the EGS in woody plants and grasslands, respectively, whereas the explanatory power for each driving factor on cultivated land was very weak. Furthermore, the EGS was controlled by drought at different timescales and the dominant timescales were concentrated in 1–3 accumulated months. Grasslands were more significantly influenced by drought than woody plants at the biome level. These findings validate the significance of SGS on the EGS in the YRB as well as highlight that both drought and SGS should be considered in autumn fall phenology models for improving the prediction accuracy under future climate change scenarios. [ABSTRACT FROM AUTHOR]

Orphaned oil and gas wells represent an important environmental and economic development concern in areas where historical energy exploration and production activity pre-dated regulations on well abandonment documentation practices. From an economic development perspective, these wells pose a subsurface risk to infrastructure development, while the environmental impact of orphaned wells is largely associated with uncontrolled emissions of both fluids and gasses, most notably atmospheric methane. Often neglected in regulatory oversight, methane emissions from orphaned wells contribute to global greenhouse gas concentrations and without proper mitigation, these emissions contribute to and further exacerbate global climate change processes. An inherent challenge of remediation efforts targeting orphaned wells is the consistent observation that only a fraction of located and identified wells produce the majority of methane emissions, yet no methodology exists to effectively separate out heavy emitters without visiting and assessing each and every well. We demonstrate that emitting wells have certain defined characteristics largely pertaining to the presence and integrity of metal casing to the surface, which can be distinguished as an organized high intensity magnetic anomaly in low-altitude UAV-based aeromagnetic surveys. In this paper, we present a database of identified and ground-truthed wellsites correlated to their magnetic signatures and provide a roadmap to initial prioritization of wellsites for subsequent remediation activities that can be implemented in complex environments where other survey options are not feasible. In a regulatory environment where resources dedicated to wellsite remediation are limited, we propose implementing this methodology as a key element of a targeted approach to wellsite prioritization for subsequent remediation activity. [ABSTRACT FROM AUTHOR]

The goal of the third LIFE project (2006-2011) [[21]] was the reintroduction of the Iberian lynx to some select areas of Andalucía (southern Spain), the region holding two of the more important Iberian lynx populations at that time. In their recent paper, Van Hassel and Bovenkerk [[1]] raised the question of how we should aid wild animals in coping with climate change and they used the Iberian lynx ( I Lynx pardinus i ) as a case study. References 1 van Hassel F., Bovenkerk B. How should we help wild animals cope with climate change? Comment on van Hassel, F.; Bovenkerk, B. How Should We Help Wild Animals Cope with Climate Change?. [Extracted from the article]

Wheat seed classification is a critical task for ensuring crop quality and yield. However, the characteristics of wheat seeds can vary due to variations in climate, soil, and other environmental factors across different years. Consequently, the present classification model is no longer adequate for accurately classifying novel samples. To tackle this issue, this paper proposes an adaptive domain feature separation (ADFS) network that utilizes hyperspectral imaging techniques for cross-year classification of wheat seed varieties. The primary objective is to improve the generalization ability of the model at a minimum cost. ADFS leverages deep learning techniques to acquire domain-irrelevant features from hyperspectral data, thus effectively addressing the issue of domain shifts across datasets. The feature spaces are divided into three parts using different modules. One shared module aligns feature distributions between the source and target datasets from different years, thereby enhancing the model's generalization and robustness. Additionally, two private modules extract class-specific features and domain-specific features. The transfer mechanism does not learn domain-specific features to reduce negative transfer and improve classification accuracy. Extensive experiments conducted on a two-year dataset comprising four wheat seed varieties demonstrate the effectiveness of ADFS in wheat seed classification. Compared with three typical transfer learning networks, ADFS can achieve the best accuracy of wheat seed classification with small batch samples updated, thereby addressing new seasonal variability. [ABSTRACT FROM AUTHOR]

Given the energy crisis and climate change due to pollution, and given that the largest emissions of greenhouse gases are produced by the energy industry, we must turn our attention to the efficient use of solar energy, which is the cleanest and most abundant of all renewable energies. In this paper, based on an analysis of the specialized literature, we studied the effect of dust accumulation on the surface of photovoltaic modules on some performance characteristics and on the efficiency of these panels and modules compared to the efficiency of clean modules. We analyzed the cause of dust accumulation and the influence of the tilt angles of the photovoltaic panels on the dust deposition rate. We highlighted the influence of atmospheric temperature, solar radiation, wind speed, and relative humidity depending on the density of the dust deposited on the surface of the photovoltaic panel, and we found a decrease in the efficiency of the panel based on the increase in dust density for slightly high values of solar radiation, wind speed, and relative humidity. We highlighted the reduction in CO2 emissions by replacing electricity from fossil fuels with solar energy. The efficient use of solar energy is a solution for the decarbonization of the energy sector. [ABSTRACT FROM AUTHOR]

Simple Summary: It is widely accepted that climate change is a rapidly increasing threat to the aquaculture industry, with the biggest issue being a slow but continuous rise in water temperature and an associated decrease in dissolved oxygen in some areas. In New Zealand fish farms, during the hottest period of the year (January/February), increased water temperatures have been shown to be detrimental to Chinook salmon performance and are associated with mortalities. We report on the responses of Chinook salmon to a chronic, long-term increase in temperature and decrease in oxygen over a three-month period. We also identified, characterised, and developed relevant biomarkers for Chinook salmon and applied them to monitor responses to changes in the fish's environment. This paper provides evidence on how the use of molecular tools, alongside general health and haematological parameters, have the potential to better inform the aquaculture industry of stock health status, thereby enhancing resilience, efficiency, and productivity. In New Zealand, during the hottest periods of the year, some salmon farms in the Marlborough Sounds reach water temperatures above the optimal range for Chinook salmon. High levels of mortality are recorded during these periods, emphasising the importance of understanding thermal stress in this species. In this study, the responses of Chinook salmon (Oncorhynchus tshawytscha) to chronic, long-term changes in temperature and dissolved oxygen were investigated. This is a unique investigation due to the duration of the stress events the fish were exposed to. Health and haematological parameters were analysed alongside gene expression results to determine the effects of thermal stress on Chinook salmon. Six copies of heat shock protein 90 (HSP90) were discovered and characterised: HSP90AA1.1a, HSP90AA1.2a, HSP90AA1.1b, HSP90AA1.2b, HSP90AB1a and HSP90AB1b, as well as two copies of SOD1, named SOD1a and SOD1b. The amino acid sequences contained features similar to those found in other vertebrate HSP90 and SOD1 sequences, and the phylogenetic tree and synteny analysis provided conclusive evidence of their relationship to other vertebrate HSP90 and SOD1 genes. Primers were designed for qPCR to enable the expression of all copies of HSP90 and SOD1 to be analysed. The expression studies showed that HSP90 and SOD1 were downregulated in the liver and spleen in response to longer term exposure to high temperatures and lower dissolved oxygen. HSP90 was also downregulated in the gill; however, the results for SOD1 expression in the gill were not conclusive. This study provides important insights into the physiological and genetic responses of Chinook salmon to temperature and oxygen stress, which are critical for developing sustainable fish aquaculture in an era of changing global climates. [ABSTRACT FROM AUTHOR]

The problem of global warming and climate change has attracted global attention, and reducing the concentration of CO2 in the atmosphere is an important step towards solving the problem. This paper mainly introduces the current development status, research hotspots, challenges and some emerging technologies of carbon capture, utilization and storage (CCUS). Among CO2 capture technologies, solvent absorption technology is currently the most mature and widely used technology, among which ionic liquid technology has great application prospects because its molecular structure can be designed and different functional groups can be connected. The surface functionalization of metal–organic frameworks in the adsorption method endows them with excellent CO2 adsorption capacity. In CO2 transportation, temperature and pressure must be considered in pipeline transportation, because they will affect the phase state of CO2 transportation. The impact of impurities on CO2 pipeline transportation is a challenge that affects pipeline design and transportation safety. In CO2 utilization, the key to enhanced oil recovery, gas recovery and displacement of coalbed methane is to increase the recovery rate and increase the storage capacity at the same time. Only by strengthening the research on the adsorption behavior between CO2 and CH4 and revealing the relevant mechanism can innovative technologies be developed. The chemical utilization of CO2 has formed many routes, but they all lack certain advantages. Most scholars are working on catalysts for CO2 conversion, especially copper-based catalysts that can convert CO2 into methanol. The conversion rate of CO2 can be effectively increased through doping or process improvement. The coupling of electrocatalytic technology and renewable energy is an important development direction in the future. In CO2 storage, geological storage is currently the most important method, especially in saline aquifers. There are currently critical issues concerning reservoir integrity and leakage potential that should be further investigated. CO2 leakage will cause serious environmental problems, and the common monitoring methods are reviewed and discussed in this paper. Finally, the research status, hotspots and cooperation networks of CCUS are summarized by using CiteSpace software in order to help the development of CCUS technology. In addition, through the review and analysis, it is found that CCUS is faced with challenges such as low capture efficiency, difficulties in transformation and utilization, high operating costs, lack of strong support policies, and lack of international cooperation, which restrict the further development of CCUS. [ABSTRACT FROM AUTHOR]

Flood events have caused huge disasters with regard to human life and economic development, especially short-term flood events that have occurred in recent years. Gravity Recovery and Climate Experiment (GRACE) satellites can directly detect the spatiotemporal characteristics of terrestrial water storage anomalies (TWSA), which play an important role in capturing flood signals. However, the monthly resolution of GRACE-derived TWSA limits its application in monitoring sub-monthly flood events. Therefore, this paper first reconstructs the daily TWSA based on a statistical model with near real-time precipitation and temperature as input variables, and then three daily flood monitoring indexes are developed based on the reconstructed TWSA. Furthermore, these indexes are employed to evaluate the temporal and spatial characteristics of the 2016 short-term flood event in the Haihe River basin (HRB), including the flood potential index (FPI), water storage deficit index (WSDI), and combined climate deviation index (CCDI). In contrast to previous studies, the temporal resolution of TWSA-based indexes is improved from the monthly scale to the daily scale, which largely improves the temporal characterization of flood monitoring. Results demonstrate that (1) among ten kinds of "Temperature-Precipitation" combinations, the reconstructed TWSA based on CN05.1-CN05.1 match well with the GRACE TWSA, as well as publicly available daily TWSA datasets with a Nash-Sutcliffe efficiency coefficient (NSE) of 0.96 and 0.52 ~ 0.81 respectively. (2) The short-term flood characteristics can be better characterized by the reconstructed daily TWSA based on CN05.1-CN05.1, reaching the peak of 216.19 mm on July 20 in the flood center. Additionally, the spatial characteristics of the equivalent water height (EWH) are detected to evolve from southwest to northeast during the short-term flood. (3) FPI, WSDI, and CCDI are proven to be effective in monitoring flood events in the HRB, which validates the reliability of the reconstructed daily TWSA. Moreover, compared to the 56% and 66% coverage of damage quantified by FPI and CCDI, the 45% damage coverage of the flood mapped by WSDI is more consistent with the governmental reports within the HRB. This paper is expected to provide a valuable reference for the assessment of short-term events caused by extreme climate change. [ABSTRACT FROM AUTHOR]

Cities, and in particular those in coastal low-lying areas, are becoming increasingly susceptible to climate change, the impact of which is worsened by the tendency for population concentration in these areas. Therefore, comprehensive early warning systems are necessary to minimize harm from extreme climate events on communities. Ideally, such a system would allow all stakeholders to acquire accurate up-to-date information and respond effectively. This paper presents a systematic review that highlights the significance, potential, and future directions of 3D city modelling, early warning systems, and digital twins in the creation of technology for building climate resilience through the effective management of smart cities. In total, 68 papers were identified through the PRISMA approach. A total of 37 case studies were included, among which (n = 10) define the framework for a digital twin technology, (n = 14) involve the design of 3D virtual city models, and (n = 13) entail the generation of early warning alerts using the real-time sensor data. This review concludes that the bidirectional flow of data between a digital model and the real physical environment is an emerging concept for enhancing climate resilience. However, the research is primarily in the phase of theoretical concepts and discussion, and numerous research gaps remain regarding the implementation and use of a bidirectional data flow in a true digital twin. Nonetheless, ongoing innovative research projects are exploring the potential of digital twin technology to address the challenges faced by communities in vulnerable areas, which will hopefully lead to practical solutions for enhancing climate resilience in the near future. [ABSTRACT FROM AUTHOR]

Affected by global warming, the frequency of crop pests and diseases have increased, causing huge losses to agricultural production. To better grasp the development and trends of research on the effects of climate change on crop pests and diseases, the literature on the impact of climate change on crop pests and diseases published from 1990 to 2021 in the Web of Science (WOS) core collection database was used. This study explores the literature characteristics and hotspot evolution through the bibliometric visualization analysis software COOC, VOSviewer, and CiteSpace, with a view to identifying the changing characteristics and trends of research changes in this field. The results showed that the number of literature on the impact of climate change on crop pests and diseases increased rapidly. The main fields involved include environmental sciences, ecology, and agronomy. Papers in these fields mainly published in journals, such as PLos One, Forest Ecology and Management, and Frontiers in Plant Science. The country with the highest number of publications was the United States, followed by China and Australia. The most prolific authors in the top 20 are research scholars from China. The first author of the top 20 highly cited papers was from the United States. It was found that that current research on the impact of climate change on crop pests and diseases mainly focuses on agricultural production and food safety. Modelling and crop growth has maintained steady development. At present, research in this field mainly focuses on pest management strategies under the impact of climate change, the response of single species, and the complex ecological mechanisms behind the response. This study provides unique insights into the research field of the impact of climate change on crop pests and diseases and provides a reference direction for future research development in this field. [ABSTRACT FROM AUTHOR]

Climate change has drawn the attention not only of scientists but of politicians and societies worldwide. The aim of this paper is to present a method for selecting research studies on climate change, waste management and the role of microbes in the recycling of organic matter in soil that analyze the role of organic agriculture as the main connection between agricultural losses and climate change. VOSviewer version 1.6.18 free software tool was used in this study in order to achieve the bibliometric and mapping approach for studies on the effects of climate change in terms of soil recycled organic matter and microbe interaction. Scopus database (accessed 29 September 2022) indexed a total of 1,245,809 bibliographic items classified into paradigms. The presented documents were downloaded from Scopus as graph-based maps and as distance-based maps in order to reflect the strength of the relation between the items. Climate change includes changes in soil and soil microorganisms as affected by natural climate variations and local weather, which have beneficial or negative effects on soil organic matter. From the examination of the selected papers, it was concluded that climate change and changing precipitation patterns are having an impact on microorganisms, particularly bacterial groups, and thus ecosystem function. [ABSTRACT FROM AUTHOR]

The alpine inland lake dynamics have been good indicators of changes in terrestrial hydrological cycles under global climate change. However, the relationship between alpine inland lake and climatic factors remained largely uncertain. This study examines the spatial-temporal change of the fluctuation of the lake by using dense time series Landsat TM/ETM/OLI images to delineate water boundary information based on the Random Forest algorithm and using ICESat (Ice, Cloud and land Elevation Satellite) dataset to monitor changes in variations of water level. Variations of Qinghai Lake (QHL) were analyzed from 1987 to 2020 and the mechanism of these changes was discussed with meteorological data. The results indicated that the QHL fluctuated strongly showing a pattern of shrinkage–expansion over the last three decades. The lake storage significantly decreased by −2.58 × 108 m3·yr−1 (R2 = 0.86, p < 0.01) from 1989 to 2004 and sharply increased (6.92 × 108 m3·yr−1, R2 = 0.92, p < 0.01) after 2004. The relationship between the lake and climate over the last 30 years implies that the decreasing evaporation and increasing precipitation were the major factors affecting the fluctuation of lake storage. Meanwhile, the temporal heterogeneity of the driving mechanism of climate change led to the phased characteristics of lake storage change. In detail, obvious warming led to the shrinkage of the QHL before 2004 through increasing evaporation, while humidifying and accelerating wind stilling dominated the expansion of the QHL after 2004 by increasing precipitation and decreasing evaporation. This paper indicated that the frameworks of multi-source remote sensing and accurate detection of water bodies were required to protect the high-altitude lakes from further climate changes based on the findings of this paper of the QHL recently. The framework presented herein can provide accurate detection and monitoring of water bodies in different locations in the Qinghai-Tibet Plateau, and provide a necessary basis for future political activities and decisions in terms of sustainable water resource management. [ABSTRACT FROM AUTHOR]

Global climate change is already affecting the environment, as glaciers are receding, ice on rivers and lakes is melting, plant and animal range's have altering, and trees are blooming early. Therefore, focus has shifted towards sustainable materials. There is a growing desire for materials that have a unique combination of qualities that metals, polymers, and other materials cannot provide, therefore scientists are turning their focus to green composites. Green composites offer a wide range of uses in automotive, aerospace, and marine applications. Composites are multiphase resources with separate interfaces that contain chemically different materials. Composites are made up of a variety of materials that are distinct in nature, and they give a set of desirable features that are superior to those of their predecessors or parents. Natural fibers are less expensive, more readily available, rust-resistant, plentiful, nontoxic, and safe for human skin, eyes, and respiratory systems. Green composites are created by combining renewable fibers with polymers (matrix) to create a new class of composites known as "green composites." This review includes studies on various animal-based fibers and their applications. In this article, recent advancements in the field of these fibers and their composites of fibers are also discussed. The physical, chemical, and mechanical properties are also discussed in this paper. Moreover, the benefits and drawbacks of using these fibers are also discussed in detail. Finally, the paper gives an outline of the topic. The results from composites constructed from each fiber are provided, along with appropriate references for more in-depth analysis studies. This review is specially performed to strengthen the knowledge bank of the young researchers working in the field of natural composites. [ABSTRACT FROM AUTHOR]

Unequivocal change in the climate system has put coastal regions around the world at increasing risk from climate-related hazards. Monitoring the coast is often difficult and expensive, resulting in sparse monitoring equipment lacking in sufficient temporal and spatial coverage. Thus, low-cost methods to monitor the coast at finer temporal and spatial resolution are imperative for climate resilience along the world's coasts. Exploiting such low-cost methods for the development of early warning support could be invaluable to coastal settlements. This paper aims to provide the most up-to-date low-cost techniques developed and used in the last decade for monitoring coastal hazards and their forcing agents via systematic review of the peer-reviewed literature in three scientific databases: Scopus, Web of Science and ScienceDirect. A total of 60 papers retrieved from these databases through the preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol were analysed in detail to yield different categories of low-cost sensors. These sensors span the entire domain for monitoring coastal hazards, as they focus on monitoring coastal zone characteristics (e.g., topography), forcing agents (e.g., water levels), and the hazards themselves (e.g., coastal flooding). It was found from the meta-analysis of the retrieved papers that terrestrial photogrammetry, followed by aerial photogrammetry, was the most widely used technique for monitoring different coastal hazards, mainly coastal erosion and shoreline change. Different monitoring techniques are available to monitor the same hazard/forcing agent, for instance, unmanned aerial vehicles (UAVs), time-lapse cameras, and wireless sensor networks (WSNs) for monitoring coastal morphological changes such as beach erosion, creating opportunities to not only select but also combine different techniques to meet specific monitoring objectives. The sensors considered in this paper are useful for monitoring the most pressing challenges in coastal zones due to the changing climate. Such a review could be extended to encompass more sensors and variables in the future due to the systematic approach of this review. This study is the first to systematically review a wide range of low-cost sensors available for the monitoring of coastal zones in the context of changing climate and is expected to benefit coastal researchers and managers to choose suitable low-cost sensors to meet their desired objectives for the regular monitoring of the coast to increase climate resilience. [ABSTRACT FROM AUTHOR]