Global Warming Research Articles

Addressing the climate crisis: The synergy of AI and electric vehicles in combatting global warming

The climate catastrophe is a critical issue that requires quick and inventive solutions. The synergistic interaction between artificial intelligence (AI) and electric automobiles is one viable path for reducing global warming. This article investigates the relationship between AI and EVs in combating climate change. We begin by highlighting the severity and urgency of the climate situation, stressing the need for adequate mitigation solutions. We then investigate the role of electric cars as a greener alternative to typical fossil fuel vehicles, emphasizing their potential to cut greenhouse gas emissions in the transportation industry. Furthermore, we look at how AI technology might improve the efficiency and performance of electric cars, such as predictive maintenance, charging infrastructure optimization, and traffic management. By harnessing AI capabilities, EVs may function more efficiently, resulting in a considerable decrease in carbon emissions. Through this research, we highlight the necessity of cooperation between the AI and EV sectors and the need for supportive governmental frameworks to expedite the adoption of these technologies. Finally, this article argues for the revolutionary power of the AI-EV synergy in addressing global warming and creating a more sustainable future.

Promotion of CO2 assimilation by stopping NP elimination is best method to stop global warming

Stop the electricity to stop the elimination of nitrogen and phosphorous. Stop the addition of ammonia at exit gas of electricity generation plant. Abandon the law about bon fire. Throw radioactive substance to deep sea. If these four items are done global warming will stop.

Quantify the Influence of Coral Reef Bleaching on Tourism in Small Island Developing States (SIDS)

As global climate change keeps deteriorating, adverse effects have emerged, including global warming, rising sea levels, and extreme weather. From diverse literature, we summarized that these effects have triggered irretrievable damage to coral reefs worldwide and indirectly influenced tourism in small island developing states (SIDS). Since the economy depends drastically on tourism, we propose the study to quantify the effect caused by coral bleaching, a by-product of global warming, on tourism in SIDS. We schedule to find the relationship between coral bleaching rate and tourism development in targeted areas via a panel data model and a regression model. We expect our result can contribute to part of the ministries' legislation in SIDS

Experimental study of heat pipes for battery cooling technology in EVs

Abstract The modern world is moving towards electric vehicles (EV) due to the increment in greenhouse gas (GHG) emissions, global warming, and the lack of fossil fuels. EVs can overcome these issues by using batteries instead of fuel. But increasing and maintaining the batteries is a major challenge in EVs because of the large heat emissions from the batteries. In order to overcome these issues and increase the performance of the batteries, a heat pipe (HP) is attached to the passive cooling system. This study aims to improve the performance of batteries and the thermal conductivity of HP with a combination of refrigerant and nanofluid (nanorefrigerant) as working fluids. Copper HP with R-134a or SWCNT is selected for this study. The thermal resistance and thermal conductivity of HP with R-134a and SWCNT were observed for several heat conditions. From the study, it was well observed that changing the working fluid inside the HP affects the thermal performance and the cooling capacity of batteries. Fixing an HP to a battery would decrease the battery’s temperature effectively. Furthermore, increasing the heat power in an evaporator section decreases the thermal resistance and enhances thermal conductivity with the shortest time limit because of Brownian motion.

Mesomorphology of clathrate hydrates from molecular ordering.

Clathrate hydrates are crystals formed by guest molecules that stabilize cages of hydrogen-bonded water molecules. Whereas thermodynamic equilibrium is well described via the van der Waals and Platteeuw approach, the increasing concerns with global warming and energy transition require extending the knowledge to non-equilibrium conditions in multiphase, sheared systems, in a multiscale framework. Potential macro-applications concern the storage of carbon dioxide in the form of clathrates, and the reduction of hydrate inhibition additives currently required in hydrocarbon production. We evidence porous mesomorphologies as key to bridging the molecular scales to macro-applications of low solubility guests. We discuss the coupling of molecular ordering with the mesoscales, including (i) the emergence of porous patterns as a combined factor from the walk over the free energy landscape and 3D competitive nucleation and growth and (ii) the role of molecular attachment rates in crystallization-diffusion models that allow predicting the timescale of pore sealing. This is a perspective study that discusses the use of discrete models (molecular dynamics) to build continuum models (phase field models, crystallization laws, and transport phenomena) to predict multiscale manifestations at a feasible computational cost. Several advances in correlated fields (ice, polymers, alloys, and nanoparticles) are discussed in the scenario of clathrate hydrates, as well as the challenges and necessary developments to push the field forward.

Continuous warming drives the colonization dynamics of periphytic ciliate fauna in marine environments.

In order to evaluate the influence of global warming on the ecosystem processes in marine environments, the changes in colonization dynamics of periphytic microbiota were studied using the periphytic ciliate communities as the test organism fauna under a continuous warming gradient of 22℃ (control), 25℃, 28℃, 31℃, and 34 ℃. The results demonstrated that (1) the test ciliate communities generally showed a similar temporal pattern in within the colonization process under the water temperatures from 22 up to 28℃; however, (2) the colonization dynamics were significantly changed, and the fitness of colonization curves to the MacArthur-Wilson model equation was failed under the temperature increased by 6 ℃, and (3) the loading or assimilative capacity of the test aquatic ecosystem was decreased with the increase of water temperature. Therefore, this study suggests that continuous warming may significantly drive the colonization dynamics of periphytic ciliates in marine ecosystems.

Robust Forest Fire Detection Method for Surveillance Systems Based on You Only Look Once Version8 and Transfer Learning Approaches

Forest fires have emerged as a significant global concern, exacerbated by both global warming and the expanding human population. Several adverse outcomes can result from this, including climatic shifts and greenhouse effects. The ramifications of fire incidents extend widely, impacting human communities, financial resources, the natural environment, and global warming. Therefore, timely fire detection is essential for quick and effective response and not to endanger forest resources, animal life, and the human economy. This study introduces a forest fire detection approach utilizing transfer learning with the YOLOv8 (You Only Look Once version 8) pretraining model and the TranSDet model, which integrates an improved deep learning algorithm. Transfer Learning based on pre-trained YoloV8 enhances a fast and accurate object detection aggregate with the TranSDet structure to detect small fires. Furthermore, to train the model, we collected 5200 images and performed augmentation techniques for data, such as rotation, scaling, and changing due and saturation. Small fires can be detected from a distance by our suggested model both during the day and at night. Objects with similarities can lead to false predictions. However, the dataset augmentation technique reduces the feasibility. The experimental results prove that our proposed model can successfully achieve 98% accuracy to minimize catastrophic incidents. In recent years, the advancement of deep learning techniques has enhanced safety and secure environments. Lastly, we conducted a comparative analysis of our method’s performance based on widely used evaluation metrics to validate the achieved results.

Group 2i Isochrysidales thrive in marine and lacustrine systems with ice cover

Global warming is causing rapid changes to the cryosphere. Predicting the future trajectory of the cryosphere requires quantitative reconstruction of its past variations. A recently identified sea-ice-associated haptophyte, known as Group 2i Isochrysidales, has given rise to a new sea-ice proxy with its characteristic alkenone distributions. However, apart from the occurrence of Group 2i Isochrysidales in regions with sea ice, and the empirical relationship between C37:4 alkenone abundance and sea-ice concentration, little is known about the ecology of these haptophyte species. Here, we systematically mapped the spatial and temporal occurrence of known Group 2i Isochrysidales based on environmental DNA in both marine and lacustrine environments. Our results indicate Group 2i is widely distributed in icy marine and lacustrine environments in both Northern and Southern Hemisphere, but is absent in warm environments. Temporally, Group 2i is part of the sea-ice algae bloom during the cold seasons, in contrast to other Isochrysidales that bloom in open waters during warm seasons. Our results indicate that ice is a prerequisite for the occurrence of the psychrophilic Group 2i haptophytes in marine and lacustrine ecosystems and further affirms its value for past ice reconstructions.

Global Warming Reduction Proposal Assessment

The aim of this work, is to find out if there is a convergence of different scientific considerations into a common aggregation point, that can be used as a key point for a new global approach to the problem of global warming, even using a heuristic method of evaluation.

Intensified surface chlorophyll responses to the Indian ocean dipole under greenhouse warming

Abstract The Indian Ocean Dipole (IOD) has been proposed to be a key driver of biological processes in the Indian Ocean (IO) in the present climate. Given the expected influence of global warming on both the properties of the IOD and the biogeochemistry within the IO, a key question arises: How will the relationship between the IOD and surface chlorophyll evolve in a warming climate? Here, utilizing simulations from the Coupled Model Intercomparison Project (CMIP) Phase 6 Earth System models, our findings reveal a notable intensification in the IOD-chlorophyll relationship under greenhouse warming. This intensification is linked to an increase in surface chlorophyll during the June to November period of positive IOD years in the southeastern IO (SEIO). Interestingly, our analysis indicates a substantial rise in IOD-related chlorophyll levels in a warming climate, despite a marked decrease in IOD-induced upwelling in the SEIO. The shallower thermocline leads to an increase in the mean nutrient concentration in the subsurface layer, thereby facilitating an enhanced anomalous nutrient supply to the surface layer, which contributes to increased surface chlorophyll. Our study highlights the consequential effects of IOD on chlorophyll dynamics and underscores the need for improved coupled models to advance our understanding of biophysical interactions in the IO in response to global warming.

Defense Pathways of Wheat Plants Inoculated with Zymoseptoria tritici under NaCl Stress Conditions: An Overview

Due to being sessile, plants develop a broad range of defense pathways when they face abiotic or biotic stress factors. Although plants are subjected to more than one type of stress at a time in nature, the combined effects of either multiple stresses of one kind (abiotic or biotic) or more kinds (abiotic and biotic) have now been realized in agricultural lands due to increases in global warming and environmental pollution, along with population increases. Soil-borne pathogens, or pathogens infecting aerial parts, can have devastating effects on plants when combined with other stressors. Obtaining yields or crops from sensitive or moderately resistant plants could be impossible, and it could be very difficult from resistant plants. The mechanisms of combined stress in many plants have previously been studied and elucidated. Recent studies proposed new defense pathways and mechanisms through signaling cascades. In light of these mechanisms, it is now time to develop appropriate strategies for crop protection under multiple stress conditions. This may involve using disease-resistant or stress-tolerant plant varieties, implementing proper irrigation and drainage practices, and improving soil quality. However, generation of both stress-tolerant and disease-resistant crop plants is of crucial importance. The establishment of a database and understanding of the defense mechanisms under combined stress conditions would be meaningful for the development of resistant and tolerant plants. It is clear that leaf pathogens show great tolerance to salinity stress and result in pathogenicity in crop plants. We noticed that regulation of the stomata through biochemical applications and some effort with the upregulation of the minor gene expressions indirectly involved with the defense mechanisms could be a great way to increase the defense metabolites without interfering with quality parameters. In this review, we selected wheat as a model plant and Zymoseptoria tritici as a model leaf pathogen to evaluate the defense mechanisms under saline conditions through physiological, biochemical, and molecular pathways and suggested various ways to generate tolerant and resistant cereal plants.

Thermal performance augmentation of honeycomb metal matrix embedded phase change material in shell-tube latent heat storage unit

Due to the global warming challenge, there is a transition from the utilization of fossil fuels to harvesting new and renewable energy sources globally. Solar energy, being the infinite source with easy accessibility, leads the race among other allied sources. However intermittent nature of it demands an efficient thermal energy storage system. In the present experimental study, an innovative heat transfer augmentation technique i.e. aluminium honeycomb grill and fine mesh embedded in PCM is demonstrated. Both visualization of PCM melt fraction and capture of temperature contour complemented each other to identify the honeycomb version (HC-LHSU) as substantially better (43 % improvement in charge period) than the base model (BM-LHSU). On the other side, due to fine pores (porosity = 95 %), the fine meshmodel (FM-LHSU) yielded poorer results than BM-LHSU. Even the temperature gradient that exists during the charge or discharge period is gauged and recognized HC-LHSU as the best, followed by BM-LHSU and FM-LHSU. Since the onset of the phase transition zone reflects heat storage rate, the same is identified, and the charge period (sensible heat + latent heat) assessed to be 8000 s, 10000 s, and 12,000 s for HC-LHSU, BM-LHSU, and FM-LHSU, respectively. Further, an improvement in the average Nusselt number by 74 % and 67 % during the charge and discharge period respectively compared to the base model justified the honeycomb grill's role in augmenting the energy storage unit's performance.

A new Hyper-heuristic based on Adaptive Simulated Annealing and Reinforcement Learning for the Capacitated Electric Vehicle Routing Problem

Electric vehicles (EVs) have been adopted in urban areas to reduce environmental pollution and global warming due to the increasing number of freight vehicles. However, there are still deficiencies in routing the trajectories of last-mile logistics that continue to impact social and economic sustainability. For that reason, in this paper, a hyper-heuristic (HH) approach called Hyper-heuristic Adaptive Simulated Annealing with Reinforcement Learning (HHASARL) is proposed. It is composed of a multi-armed bandit method and the self-adaptive Simulated Annealing (SA) metaheuristic algorithm for solving the problem called Capacitated Electric Vehicle Routing Problem (CEVRP). Due to the limited number of charging stations and the travel range of EVs, the EVs must require battery recharging moments in advance and reduce travel times and costs. The implementation of the HH improves multiple minimum best-known solutions and obtains the best mean values for some high-dimensional instances for the proposed benchmark for the IEEE WCCI2020 competition.

Scenario Analysis of CO2 Reduction Potentials from a Carbon Neutral Perspective

As a major emitter of CO2, China needs to take responsibility for slowing down global warming. In this paper, the potential carbon emission intensity of provinces is firstly calculated using the non-radial directional distance function under the group- and meta-frontier techniques, and then six scenarios based on two factors (economic development and carbon intensity) are set up to estimate the emission reduction potential of China and each province. Considering the goal of carbon neutrality, the calculation of CO2 emission reduction potential quantifies the amount of emissions that can be reduced and the amount of emissions that should be balanced. Additionally, the degree of difficulty in achieving abatement potential is also calculated. The findings are as follows: First, assuming that the economic growth rate is reduced to 4.4% (achieving the second “100-year goal”) and each province adopts the most advanced low-carbon technologies, China could reduce carbon emissions by 5970.56 Mt compared to 2019 levels. To achieve net-zero emissions, the remaining 3824.2 Mt of carbon emissions should be removed by carbon reduction technologies. Second, the effect of slowing down economic growth and decreasing carbon intensity varies greatly among provinces. Hebei and Shandong should be prioritized as they have the greatest potential for emission reductions under both scenarios. Third, it is more difficult for Beijing, Shanghai, Hubei, Hunan, Inner Mongolia Autonomous Region, Chongqing, and Sichuan to achieve the abatement potential and they require more effort to reduce the same amount of carbon emissions compared to other provinces. The study provides a reference for achieving carbon neutrality and helps provinces to develop differentiated emission reduction strategies.

Impacts of Climate Change on Logistics and Supply Chains

While the global public is struggling with Covid-19 and the subsequent inflation, war and energy crisis, climate change has turned into a forgotten crisis. However, with the increasing number of disasters caused by extreme weather events in different continents of the world, climate change has started to attract attention all over the world. Extreme weather events such as global warming, floods and hurricanes, which are the result of global climate change, not only affect the lives of societies, but also affect supply chains. The first spots to see the effects of extreme weather conditions as a result of climate change are the production centers in different parts of the world and the international transportation sector, which ensures that the goods produced in these facilities reach the markets. By this study, the qualitative effects of climate change on supply chains were investigated and this research is expected to shed light on the effects of climate in international logistics activities.

Engineering Gut Symbionts: A Way to Promote Bee Growth?

Bees play a crucial role as pollinators, contributing significantly to ecosystems. However, the honeybee population faces challenges such as global warming, pesticide use, and pathogenic microorganisms. Promoting bee growth using several approaches is therefore crucial for maintaining their roles. To this end, the bacterial microbiota is well-known for its native role in supporting bee growth in several respects. Maximizing the capabilities of these microorganisms holds the theoretical potential to promote the growth of bees. Recent advancements have made it feasible to achieve this enhancement through the application of genetic engineering. In this review, we present the roles of gut symbionts in promoting bee growth and collectively summarize the engineering approaches that would be needed for future applications. Particularly, as the engineering of bee gut symbionts has not been advanced, the dominant gut symbiotic bacteria Snodgrassella alvi and Gilliamella apicola are the main focus of the paper, along with other dominant species. Moreover, we propose engineering strategies that will allow for the improvement in bee growth with listed gene targets for modification to further encourage the use of engineered gut symbionts to promote bee growth.

Reducing global warming and mitigating PM2.5 air pollution with electrorheology

Black carbon, a part of harmful PM2.5 air pollution, is the second largest contributor to global warming. Due to per unit of mass, black carbon has a warming impact on climate 460–1,500 times stronger than CO2. UNEP emphasizes that the black carbon issue is more urgent than CO2 now. Unfortunately, current technologies cannot help large factories in preventing emission of black carbon particles into air because they fail to catch them effectively. Therefore, a new technology, which can catch black carbon and other PM2.5 pollution effectively, is critically needed. Here we report that electrorheology is the solution we are looking for. Under a strong electric field, black carbon and other particles are polarized. When the polluted air flow becomes a special flow with pollution particles moving adjacent to the electrodes, black carbon and other particles are captured by the electrodes effectively. Our tests have confirmed that with this technology, more than 98% black carbon and other PM2.5 particles can be caught. Moreover, this technology is not only suitable as incoming air filters, but also appropriate for large factories in preventing emitting black carbon and pollution into air. We hope that electrorheology will improve our environment and reduce global warming effectively.

Evaluation of Charcoal Usage and Its Influence on Deforestation in Makurdi Metropolis Benue State, Nigeria

Charcoal is the dark grey residue consisting of impure carbon obtained from vegetation substance and produced by pyrolysis, the heating of wood in the absence of oxygen. Charcoal is considered the major source of energy for the inhabitants of Makurdi metropolis. The study was conducted to assess the consumption of charcoal in Makurdi metropolis. Five council wards were selected purposively for data collection. The selected wards are noted for having large quantities of charcoal and consumers almost all year round. The wards selected are Agan, Fiidi, Wadata, Modern Market, and North Bank. From each council ward, 20 respondents were drawn using a random sampling technique. 100 respondents were selected and interviewed using a pre-tested checklist (questionnaire) to collect primary data. From the result, Prosopis africana was the highest used tree species for charcoal with 83.5% then Vitellaria paradoxa with 7.9%. Also, 88% of the respondents preferred charcoal for cooking in providing food for the family because of its affordability compared to other cooking energy sources. Therefore, it is inferred that charcoal has a positive impact on the lives of consumers since affordability is considered the main reason why the majority of households use charcoal. However, the continuous use of forest trees threatens the future of our forest estate and biodiversity leading to land degradation, endangering of species and enhancement of global warming. Therefore, Alternative energy sources should be encouraged for household fuel to ease the pressure on charcoal.

Advances in Deep Learning Recognition of Landslides Based on Remote Sensing Images

Against the backdrop of global warming and increased rainfall, the hazards and potential risks of landslides are increasing. The rapid generation of a landslide inventory is of great significance for landslide disaster prevention and reduction. Deep learning has been widely applied in landslide identification due to its advantages in terms of its deeper model structure, high efficiency, and high accuracy. This article first provides an overview of deep learning technology and its basic principles, as well as the current status of landslide remote sensing databases. Then, classic landslide deep learning recognition models such as AlexNet, ResNet, YOLO, Mask R-CNN, U-Net, Transformer, EfficientNet, DeeplabV3+ and PSPNet were introduced, and the advantages and limitations of each model were extensively analyzed. Finally, the current constraints of deep learning in landslide identification were summarized, and the development direction of deep learning in landslide identification was analyzed. The purpose of this article is to promote the in-depth development of landslide identification research in order to provide academic references for the prevention and mitigation of landslide disasters and post-disaster rescue work. The research results indicate that deep learning methods have the characteristics of high efficiency and accuracy in automatic landslide recognition, and more attention should be paid to the development of emerging deep learning models in landslide recognition in the future.

Prediction and Optimization Analysis of the Performance of an Office Building in an Extremely Hot and Cold Region

The White Paper on Peak Carbon and Carbon Neutral Action 2022 states that China is to achieve peak carbon by 2030 and carbon neutrality by 2060. Based on the “3060 dual-carbon” goal, how to improve the efficiency of energy performance is an important prerequisite for building a low-carbon, energy-saving, green, and beautiful China. The office performance building studied in this paper is located in the urban area of Turpan, where the climate is characterized by an extremely hot summer environment and a cold winter environment. At the same time, the building is oriented east–west, with the main façade facing west, and the main façade consists of a large area of single-layer glass curtain wall, which is affected by western sunlight. As a result, there are serious problems with the building’s energy consumption, which in turn leads to excessive carbon emissions and high life cycle costs for the building. To address the above problems, this paper analyzes and optimizes the following four dimensions. First, the article creates a Convolutional Neural Network (CNN) prediction model with Total Energy Use in Buildings (TEUI), Global Warming Potential (GWP), and Life Cycle Costs (LCC) as the performance objectives. After optimization, the R2 of the three are 0.9908, 0.9869, and 0.9969, respectively, thus solving the problem of low accuracy of traditional prediction models. Next, the NSGA-II algorithm is used to optimize the three performance objectives, which are reduced by 41.94%, 40.61%, and 31.29%, respectively. Then, in the program decision stage, this paper uses two empowered Topsis methods to optimize this building performance problem. Finally, the article analyzes the variables using two sensitivity analysis methods. Through the above research, this paper provides a framework of optimization ideas for office buildings in extremely hot and cold regions while focusing on the four major aspects of machine learning, multi-objective optimization, decision analysis, and sensitivity analysis systematically and completely. For the development of office buildings in the region, whether in the early program design or in the later stages, energy-saving measures to optimize the design have laid the foundation of important guidelines.