The Upper Hunter Valley is a major coal mining area in New South Wales (NSW), Australia. Due to the ongoing increase of mining activities, PM10 (air-borne particle with an aerodynamic diameter less than 10 micrometres) pollution has become a major air quality concern in local communities. The early part of this study identified two air quality subregions in the valley. This paper aims to provide a holistic summarisation of the relationships between elevated PM10 pollution in two subregions and the local- and synoptic-scale meteorological conditions for spring and summer, when PM10 pollution is relatively high. A catalogue of twelve synoptic types and a set of six local meteorological patterns have been quantitatively derived and linked to each other using the self-organising map (SOM) technique. The complex meteorology-air pollution relationships have been visualised/interpretated on the SOM planes for two representative locations. It was found that the influence of local meteorological patterns differed significantly for mean PM10 levels vs the occurrence of elevated pollution events and between air quality subregions. In contrast, synoptic types showed generally similar relationships with mean vs elevated PM10 pollution in the valley. Two local meteorological patterns, the hot-dry-northwesterly wind conditions and the hot-dry-calm conditions, were found most PM10 pollution conducive in the valley when combined with a set of synoptic counterparts. These synoptic types are featured with the influence of eastward migrating continental high pressure system and westerly troughs, or a ridge extending northwest towards coastal northern NSW or southern Queensland from the Tasman Sea. The method and results can be used in air quality research for other locations of NSW, or similar regions elsewhere.

The Upper Hunter Valley is a major coal mining area containing approximately 40% of the currently identified total coal reserves in New South Wales (NSW), Australia. Due to the ongoing increase of mining activities, PM10 (air-borne particle with an aerodynamic diameter less than 10 micrometres) pollution has become a major air quality concern in local communities. This paper summarises the spatial and temporal variability modes of PM10 pollution in the region, based on long-term multi-site monitoring data and the application of the rotated principal component analysis (RPCA) and wavelet analysis techniques. RPCA identified two distinct air quality clusters/subregions in the valley, one in the west/northwest, the other in the southeast. Wavelet analysis revealed the annual cycle to be most persistent temporal mode of PM10 variability in both subregions, with intermittent signals observed at time scales of around 120, 30~90 and under 30 days. How these variation modes are related to the effects of local PM10 emissions and the influence of meteorology at different time scales deserves further attention in future work. The findings will be used in air quality reporting and forecasting in NSW. The methodology and results can also be useful for air quality research in similar regions elsewhere.

Off-grid electrification planning increasingly recognizes the importance of productive use of electricity (PUE) to promote community value creation and (financial) project sustainability. To ensure a sustainable and efficient integration in the community and energy system, PUE assets must carefully be evaluated to match both the community needs and the residential electricity demand patterns. We propose a novel methodology interlinking qualitative interviews, statistical analysis, and energy system modelling to optimize decision-making for PUE integration in off-grid energy systems in rural Madagascar by aligning relevant PUE effectively with anticipated residential electricity demand patterns based on socio-economic determinants of the community. We find that a possible contribution of the PUE to reducing the electricity costs depends signifi-cantly on three factors: 1. the residential electricity consumption patterns that are influenced by the socio-economic composition of the community, 2. the degree of flexibility of i) PUE assets and ii) operational preferences of the PUE user, and 3. the capacity of community members to finance and operate PUE assets. Our study demonstrates that significant cost reductions for PUE-integrated off-grid energy systems can be achieved by applying our proposed methodology. When matching PUE and residential consumption patterns, the integration of PUE assets in residential community energy system can reduce the financial risk of operators, provided the PUE enterprise operates reliably and sustainably. We highlight that the consideration of local value chains and co-creation approaches are essential to ensure the energy system is addressing the community’s needs, creates value for the community, enhances the project´s financial sustainability, and is achieving the overall objectives of decentralized energy system planning.

Climate simulations in West Africa have been attributed with large uncertainties. Global climate projections are not consistent with changes in observations at the regional or local level of the Niger basin, making management of hydrological projects in the basin uncertain. This study evaluates the potential of using the quantile mapping bias correction to improve the Coupled Model Intercomparison Project (CMIP5) outputs for use in hydrology impact studies. Rainfall and temperature projections from 8 CMIP5 Global Climate Models (GCM) were bias corrected using the quantile mapping approach. Impacts of climate change was evaluated with bias corrected rainfall, temperature and potential evapotranspiration (PET). The IHACRES hydrological model was adapted to the Niger basin and used to simulate impacts of climate change on discharge under present and future conditions. Bias correction significantly improved the accuracy of rainfall and temperature simulations compared to observations. Nash coefficient (NSE) for monthly rainfall comparisons of 8 GCMs to the observed was improved by bias correction from 0.69 to 0.84. The standard deviations among the 8 GCM rainfall data were significantly reduced from 0.13 to 0.03. Increasing rainfall, temperature, PET and river discharge were projected for all GCMs used in this study under the RCP8.5 scenario. These results will help improving projections and contribute to the development of sustainable climate change adaptation strategies.

German noise action plans aim to reduce negative health outcomes from noise exposure and identify quiet areas free of noise pollution. Quiet area identification in German noise action plans is based primarily on noise mapping and spatial analysis and not empirical or qualitative data about acoustic environments, thus leaving a gap in the understanding of the quality of formally recognized quiet areas in noise action plans. This work presents a comparative empirical case study in Dortmund, Germany, with the aim to describe the diurnal dB(A) and biophonic properties of quiet areas versus noise 'hot spots'. Sound observations were collected (n=282,764) in five different natural or recreational land use patch types larger than four acres within 33 proposed quiet areas in Dortmund (n=70) and 23 noise hot spots between April 27^th, 2022 and March 2^nd, 2023. We find that quiet areas are on average more than 20 dB(A) quieter than noise hot spots almost every hour of the day. Forests, managed tree stands, cemeteries, and agriculture diel patterns are dominated by dawn dusk chorus in spring and summer, whereas sport and recreation as well as noise hot spots are dominated by traffic and human noise. A novel composite biophony mapping procedure is presented that finds distinct temporal distribution of biophony in forested and agriculture peri-urban locations positively associated with patch size, distance away from LDEN > 55, proximity to water, and the number of vegetation layers in the plant community. Anthrophony distribution dominates urban land uses in all hours of the day, but expands during day and evening and contracts at night and in dusk hours. The procedures presented here illustrate how qualitative information regarding quiet areas can be integrated into German noise action planning.

Understanding the dynamics of Organic Carbon mineralization is fundamental in forecasting biosphere to atmosphere Net Carbon Ecosystem Exchange (NEE). With this perspective, we developed 3D-CMCC-PSM, a new version of the hybrid Process Based Model 3D‐CMCC FEM where also heterotrophic respiration (R_h) is explicitly simulated. The aim was to quantify NEE as a forward problem, by subtracting Ecosystem Respiration (R_eco) to Gross Primary Productivity (GPP). To do so, we developed a simplification of the Soil Carbon dynamics routine proposed in DNDC [1]. The method calculates decomposition as a function of soil moisture, temperature, state of the organic compartments, and relative abundance of microbial pools. Given the pulse dynamics of soil respiration, we introduced modifications in some of the principal constitutive relations involved in phenology and littering sub-routines. We quantified the model structure related uncertainty in NEE, by running our training simulations over 1000 random parameter-sets extracted from parameters distributions expected from literature. 3D-CMCC-PSM predictability was tested on independent time series for 6 Fluxnet sites. The model resulted in daily and monthly estimations highly consistent with the observed time series. It showed lower predictability in Mediterranean ecosystems, suggesting that it may need further improvements in addressing evapotranspiration and water dynamics.

Assessment of extreme events and climate change on reservoir inflow is important for water and power stressed countries. Projected climate is subject to uncertainties related to climate change scenarios and Global Circulation Models (GCMs’). Extreme climatic events will increase with the rise in temperature as mentioned in the AR5 of the IPCC. This paper discusses the consequences of climate change that include extreme events on discharge. Historical climatic and gauging data were collected from different stations within a watershed. The observed flow data was used for calibration and validation of SWAT model. Downscaling was performed on future GCMs’ temperature and precipitation data, and plausible extreme events were generated. Corrected climatic data was applied to project the influence of climate change. Results showed a large uncertainty in discharge using different GCMs’ and different emissions scenarios. The annual tendency of the GCMs’ is bi-vocal: six GCMs’ projected a rise in annual flow, while one GCM projected a decrease in flow. The change in average seasonal flow is more as compared to annual variations. Changes in winter and spring discharge are mostly positive, even with the decrease in precipitation. The changes in flows are generally negative for summer and autumn due to early snowmelt from an increase in temperature. The change in average seasonal flows under RCPs’ 4.5 and 8.5 are projected to vary from -29.1 to 130.7% and -49.4 to 171%, respectively. In the medium range (RCP 4.5) impact scenario, the uncertainty range of average runoff is relatively low. While in the high range (RCP 8.5) impact scenario, this range is significantly larger. RCP 8.5 covered a wide range of uncertainties, while RCP 4.5 covered a short range of possibilities. These outcomes suggest that it is important to consider the influence of climate change on water resources to frame appropriate guidelines for planning and management.

Despite saltmarshes being considered as nature-based solutions (NbS) towards climate change mitigation, India’s saltmarsh ecosystems are least studied. This study quantifies the seasonal variation in carbon stocks mono-specific saltmarsh species (Porteresia coarctata) and its potential to play an important role in India’s climate change mitigation plans. Seasonal (pre-and post-monsoon) sampling of P. coarctata surface water, biomass and 30 cm sediment core was collected across four locations on the east coast of India to quantify sediment dry bulk density (DBD), organic matter (OM%), organic carbon (Corg%), Corg stocks of sediment and biomass, total carbon (C%) and nitrogen (N%) and stable isotopes of δ13C and δ15N. In general, the sediment DBD, OM and Corg of P. coarctata meadows was higher in post-monsoon and was influenced by salinity and pH changes. Isotope (δ¹³C) modelling of various sediment C sources suggested, particulate organic matter (POM) contribution was highest (0.04-0.79%) followed by P. coarctata (0.01–052%) and other macrophytes across our study locations. The seasonal variation of δ¹³C showed increased contribution of marine derived particulate and dissolved organic matter into P. coarctata meadows in post-monsoon season. Heavier δ¹⁵N values were observed in post-monsoon season suggesting anthropogenic input, that was utilized by P. coarctata to increase its above and below-ground biomass and shoot density. The combined ecosystem (30 cm sediment + biomass) Corg stocks of P. coarctata was 1.7-fold higher in post-monsoon (4021.20± 917 Mg C) compared to pre-monsoon (2297.36 ±647 Mg C) season among the four locations with a sediment Corg contribution >70%. Based on the International Panel for Climate Change Tier II assessment the P. coarctata meadows (443 ha) can help in avoiding the pre and post-monsoon emissions of 8431.34 and 14757.84 Mg CO₂ respectively. The combined price of the total CO₂ equivalent stored in P. coarctata meadows in pre- and post-monsoon is US$ 14.50 and US$ 25.38 million respectively. Further studies quantifying the NbS potential of P. coarctata mono-specific and mixed meadows of India’s coast is needed along with integration of saltmarsh ecosystems into India’s National Action Plan on Climate Change.

Subnational governments play a key role responding to climate change risks in terms of policy strategies and instruments. This article analyzes how Brazilian municipal and state governments have developed and implemented public policies to mitigate and to adapt to climate change risks. We surveyed all cities’ and states’ climate policies within the country. The methodological approach includes five main points of analysis: 1. mitigation targets and intentions; 2. adaptation actions; 3. stakeholders’ participation; 4. policy implementation; 5. participation in networks related to climate change. Our results suggest that even though subnational climate policies in Brazil are isolated initiatives within the national context, they play an important role responding to climate change risks in different scales and levels. The strongest Brazilian policies with both mitigation and adaptation actions counted on previous mobilization for the climate issue involving different stakeholders from several segments of the society. These governments have also participated in transnational cooperation networks related to climate change.

This bibliographic study addresses key aspects related to fishing, product safety, and climate change in the Adriatic Sea region. The examination of product safety focuses on the assessment of contaminants originating from human activities such as industry, mining, agriculture, and household waste disposal. The contamination of the aquatic environment has emerged as a pressing global concern, extending to the Adriatic subbasin. Aquatic organisms, including fish, are prone to accumulating pollutants directly from polluted water sources and indirectly through the food web. The bio-accumulation of potentially hazardous substances, particularly heavy metals, pesticides, PCBs, PAHs, and antibiotic resistance in aquatic organisms, poses a significant threat to human health. Climate change effects will deplete our seafood supply in terms of quantity and safety owing to negative consequences such as higher levels of pollution, parasites, viruses, infections, acidification, and toxicities such as shellfish poisoning. Global food safety strategies should be developed to reduce greenhouse gas emissions and promote environmentally friendly technology, which indirectly affects seafood quality and microbiological safety, especially for Adriatic Sea, which is part of the Mediterranean Sea, characterized by the most polluted waters in the world.

The Vu Gia - Thu Bon River Basin (VG-TB) is confronting numerous challenges related to water security (WS), particularly in the face of the impacts of climate change. These challenges manifest in salinity intrusion, altered rainfall patterns, and reduced water supply in downstream areas. To assess the current state of water security in the basin, the study employed statistical analysis methods, Process Analysis Method (PAM), SMART, and Analytic Hierarchy Process (AHP) to develop a comprehensive Assessment Framework for Water Security (AFWS) in the VG-TB. The AFWS encompasses five key dimensions, 17 indicators, and 28 variables, aligning with the UN-Water definition of water security and addressing the Sustainable Development Goal 6 (SDG6) criteria for global water sustainability. It also adheres to the Asian Development Bank's (ADB) approach to water security assessment as outlined in the Asian Water Development Outlook (AWDO) reports. This allows for the computation of a comprehensive Water Security Index (WSI) for specific regions, serving as a foundation for decision-making and policy formulation aimed at enhancing water security in the context of climate change and facilitating sustainable basin development in the future.

The agronomic characteristics of Cenchrus purpureus were assessed through a randomized block experimental design with seven accessions (Taiwan grass, King grass, Elephant grass, Merkeron, CT-115, Purpule grass and Maralfalfa) as treatments and four replications research was carried out at the Instituto Tecnológico Superior de Tantoyuca, in 2019 and 2020, under rainfed conditions. Sowing density was established at 36 tillers in 25 m2. Green and dry matter yield, daily rates of dry matter accumulation in leaves, leaf/stem ratio, height, leaf area index and cutting frequency were assessed. Rainfall amount, temperature, relative humidity and global radiation levels were recorded to assess their effect on the evaluated variables. One-way ANOVA and multivariate analysis techniques, such as multiple linear regression (MLR), Clustering, and Principal component analysis (PCA) with the Statistica V7 software, were used. The leaf area index was greatest (p < 0.05) on the dry season. Green and dry matter yield were greatest in Maralfalfa (p < 0.05). Variation in the results is attributed to weather phenomena; nonetheless, Elephant grass and Maralfalfa showed the highest yields. Quality parameters must be evaluated to identify those grasses with the best attributes.

Polar stratospheric clouds (PSCs) form in polar regions typically between 15 and 25 km, when the local temperature is sufficiently low. PSCs play an important role in the ozone chemistry and the dehydration and denitrification of the stratosphere. Lidars with a depolarization channel may be used to detect and classify different classes of PSCs. The main PSC classes are water ice, nitric acid trihydrate (NAT) and supercooled ternary solutions (STS), the latter being liquid droplets consisting of water, nitric acid and sulphuric acid. PSCs have been observed at the lidar observatory at Concordia Station from 2014 on. The harsh environmental conditions at Concordia during winter render successful lidar operation difficult. In order to facilitate the operation of the observatory several measures have been put in place to achieve an almost complete remote control of the system. PSC occurrence is strongly correlated with local temperatures and is affected by dynamics, as the PSC coverage during the observation season shows.

Artificial intelligence (AI) has emerged as a powerful tool for addressing the challenges posed by climate change and their impact on public health. By leveraging its capacity to analyze and predict climatic patterns, AI offers opportunities to enhance resource management and develop effective strategies for climate change mitigation. Moreover, AI can contribute to generating sustainable solutions that address the complex and interconnected nature of climate change. For example, AI can enable the optimization of energy consumption and facilitate the integration of renewable energy sources into existing systems. It can also support the development of climate models that provide timely and accurate predictions, enabling policymakers to implement proactive measures for disaster preparedness and response. Furthermore, AI-powered disease surveillance and mitigation techniques can improve public health outcomes by identifying patterns and trends in the spread of diseases in relation to climatic factors.However, the widespread adoption of AI-based solutions is not without challenges. Ethical concerns surrounding privacy and data ownership must be addressed, as the use of AI requires access to large datasets, raising potential privacy risks. Technical constraints, such as limited computational power and the need for sophisticated algorithms, also pose obstacles to the implementation of AI in climate change mitigation strategies. Furthermore, issues related to the accessibility and affordability of AI technologies must be resolved to ensure equitable distribution and maximize its potential impact on public health.To fully harness the power of AI in addressing climate change and improving public health outcomes, it is crucial to promote innovation, multidisciplinary collaboration, and open data science. Innovation can drive the development of new AI algorithms and technologies specifically tailored to address climate change challenges. Multidisciplinary approaches that bring together experts from diverse fields, including climate science, public health, and computer science, can foster a holistic understanding of complex systems and enable the design of comprehensive solutions. Finally, open data science practices, such as sharing data and algorithms, can facilitate collaboration and accelerate progress in mitigating climate change and its public health impacts.In conclusion, AI offers promising opportunities for effectively addressing climate change and its impact on public health. However, to fully realize its potential, it is essential to tackle ethical and privacy concerns, overcome technical constraints, and ensure accessibility and affordability. By promoting innovation, multidisciplinary collaboration, and open data science, we can unlock the transformative power of AI in mitigating climate change and improving public health outcomes.

In order to achieve global climate goals, it is necessary to estimate greenhouse gases (GHG) fluxes from ecosystems. To obtain a comprehensive assessment of CO2, СН4, N2O natural fluxes for the Russian Federation we used the "bottom-up" method according to [1]. We updated estimates for forest ecosystems based on State Forest Inventory data and satellite monitoring of forest disturbances. For grassland ecosystems, it was based on the correct distribution of areas between steppe and non-steppe zones. The estimated net uptake of natural ecosystems of Russia was 1.1 ± 1.8 billion tons of CO2-eq./year. The study shows that if only CO2 is taken into account, the net absorption of terrestrial ecosystems in Russia corresponds to more than -2.5 billion tons of CO2 (35% of forests’ contribution). However, given the emissions of non-CO2 GHGs, total net absorption in Russia’s natural ecosystems is reduced to about -1 billion tons of CO2-eq (with the forests’ contribution increasing to 80%). With regard to anthropogenic fluxes, the overall balance of GHGs in Russia corresponds to net-emissions of 1 billion tons of CO2-eq/year into the atmosphere. To improve reporting under the Paris Agreement, countries should aim to include only anthropogenic ("manageable") GHG fluxes on managed land.

Oedaleus decorus asiaticus, Calliptamus abbreviatus, Angaracris rhodopa, and Myrmeleotettix palpalis are the main grasshoppers that harm the natural grassland in the Hexi Corridor. In this study, the MaxEnt model was employed to identify the key environmental factors affecting the distribution of the four grasshoppers habitats and to assess their distribution under current and future climate change conditions. The aim was to provide a basis for grasshopper monitoring, prediction, and precise control. In this study, 61 occurrences of O. decorus asiaticus, 68 occurrences of A. rhodopa, 58 occurrences of C. abbreviatus, and 92 occurrences of M. palpalis were recorded by GPS, and the distribution of suitable habitats for O. decorus asiaticus, C. abbreviatus, A. rhodopa, and M. palpalis were predicted under current and future climatic scenarios using a MaxEnt model. The average AUC and TSS values of the four grasshoppers were greater than 0.9, and the simulation results were excellent and highly reliable. The mean annual precipitation was the main factor limiting the current range of suitable areas for these four species. Under the current climate, O. asiaticus, C. abbreviatus, and A. rhodopa were mainly distributed in the central and eastern parts of the Hexi Corridor, and M. palpalis was distributed throughout the Hexi Corridor, with a potential distribution area of 1.44×104, 1.43×104, 1.29×104, and 2.12×104 km2, accounting for 15.3, 15.2, 13.7, and 22.5% of the total area of the grasslands in the Hexi Corridor, respectively. The highly suitable areas of O. asiaticus, C. abbreviatus, and A. rhodopa were mainly distributed in the east-central part of Zhangye City, the western part of Wuwei City, and the western and southern part of Jinchang City, with areas of 0.35×104, 0.29×104, and 0.20×104 km2, accounting for 3.7, 3, and 2.2% of the grassland area, respectively. The high habitat of M. palpalis was mainly distributed in the southeast of Jiuquan City, the west, middle, and east of Zhangye City, the west of Wuwei City, and the west and south of Jinchang City, with an area of 0.32×104 km2, accounting for 3.4% of the grassland area. In the 2030s, the range of O. asiaticus, C. abbreviatus, A. rhodopa, and M. palpalis was predicted to decrease.

Objective: Mechanical bowel preparation is essential for the visualisation of the colon during colonoscopy, and previous studies have identified changes in gut microbiota composition after bowel preparation and colonoscopy. Considering the gut microbiota is increasingly implicated in mood, we aimed to explore the potential impact of this intervention on mood and the microbiota-gut-brain axis. Methods: We conducted a pre-post intervention study, with timepoints of one week before and one month after bowel preparation and colonoscopy, in community-dwelling adults referred for colonoscopy. Our primary outcome was change in average Hospital Anxiety and Depression Scale depression sub-scores using generalised estimating equations. We also examined changes in average anxiety, stress, and quality of life scores, and faecal microbiota composition and potential function using 16S rRNA sequencing. We further explored associations between changes in depressive symptoms and the gut microbiota and conducted post-hoc analyses to explore potential effect modifiers. Key Results: 59 participants were included in the analyses. Depressive symptoms decreased one-month post-procedure compared to baseline (adjusted β=−0.64; 95%CI: −1.18, −0.11). Irritable bowel syndrome (IBS) appeared to moderate this relationship (1.78; 0.292, 3.26); depressive symptoms increased in those with, and decreased in those without, IBS. Changes in gut microbiome composition included reduced alpha diversity, modest effects on beta-diversity, and increases in genera associated with potential health benefits, one-month post-procedure compared to baseline. Increases in the CLR-transformed abundance of Ruminococcaceae UCG-009 were associated with improvements in depressive symptoms. Conclusions: There is preliminary evidence of a potential mental health effect of bowel preparation and colonoscopy, which may be associated with changes to the gut microbiota. There may also be a differential mental health impact of this procedure for those with and without IBS. Considering the limitations of our study, further research is required to confirm these findings and their clinical relevance.

In this study, a basic study was performed to analyze the seasonal temperature status of a research room in the Global Environment Research Building where ceiling-embedded indoor units are installed to study the room temperature status of the building as well as to improve its thermal environment. In addition, a direction for improvement of the indoor thermal environment in the winter was proposed through a CFD (computational fluid dynamics) simulation and was proven by an additional experiment. Through the results of this study, it appeared that if the ceiling-embedded indoor unit was installed in the small indoor space without considering the thermal vulnerability of its perimeter boundary, the air temperature of the upper part was greatly different from that of the bottom part in the winter. Hence, in this study, as a means to improve it, convectors were installed to minimize the effect of the external thermal environment and angle-controllable air flowing fans were installed to mitigate the stratification distribution. With such result, it was intended to present the essential data for improvement of the thermal environment as well as conservation of heating energy in the winter for buildings by reviewing the use of the ceiling-embedded indoor unit in the future.

Every year since the last fifty years, the world is waking up to higher and higher average temperature of the earth. Initially it was thought that this trend is a part of cyclical warming and cooling of the earth and nothing to be much worried about. However, with weather conditions become erratic and unusual over a large swath of the planet more and more frequently and regularly, scientists started to speak of Climate Change and people started to listen. In this paper we briefly discuss the link between global warming, climate change and current policies in the developing world to drive home the urgent need for alternative thinking. We briefly discuss the theoretical link between economic development and greenhouse gas emission and lay the micro foundation of the Environmental Kuznets Curve (EKC) before providing some evidence of global climate change and why this is a matter of concern for social scientists. Then we explore two avenues through which human activities are aggravating the situation especially in developing countries – the role of business architecture and the role of built architecture. We argue that these twin architecture are aggravating global warming by moving away from sustainable development policies.

(1) Background: A simple approach to map irrigated landcover has been introduced by using measures derived from optical spectral range as an alternative to thermal range. It has been demonstrated that substituting surface temperature (Ts, ‘thermal approach’) with SWIR transformed reflectance (STR, ‘optical approach’) to detect surface moisture is feasible with comparable results. (2) Methods: Using an iterative thresholding procedure to minimize within-class variance, the bilevel segmentation of variables derived from Landsat-8 representing surface moisture and vegetation cover was achieved for the 2020-21 summer for a key irrigation district in Australia. (3) Results: The results of irrigated landcover by optical approach found to be comparable with those by thermal approach. The classification accuracy was assessed using water delivery records at farm level. Though the overall accuracy is high in both cases, the optical approach (97.6%) performed slightly better than the thermal approach (93.9%). (4) Conclusions: The feasibility of using STR to map irrigated landcover has been confirmed by a high-level overall accuracy assessment. This has broader implications in terms of irrigated landcover assessment, as the use of satellite imagery in these applications may not necessarily be limited to microwave or thermal sensors.

Climate change, recognized as a global crisis, is not only altering our environment but also significantly impacting human health. Particularly, its influence on non-communicable diseases (NCDs), a leading cause of global mortality, is becoming increasingly evident yet underexplored. This review aims to synthesize current research on the impact of climate change on NCDs, addressing a critical gap in our understanding of how environmental changes affect human health. We examine the complex interplay between climate change and NCDs, drawing insights from recent studies that highlight the adverse effects of climate variables on chronic diseases, such as cardiovascular and kidney diseases, and the role of food and nutrition security in this nexus. Furthermore, the review explores the disproportionate impact on vulnerable regions and populations, particularly in Pacific Island countries and territories, underscoring the urgent need for context-specific solutions and a cross-sectoral approach. By integrating findings from various disciplines, this review aims to inform policy and public health interventions, highlighting the necessity of collaborative efforts to mitigate the burgeoning burden of NCDs in a rapidly changing climate.

This research aimed to investigate the relationship between climate policy uncertainty (CPU), clean energy (ENERGY), carbon emission allowance prices (CARBON), and Bitcoin returns (BTC) for the period from August 2012 to August 2022. The empirical analysis strategies utilized in this study included the Fourier Bootstrap ARDL long-term coefficient estimator, Fourier Granger Causality, and Fourier Toda Yamamoto Causality methods. Following the confirmation of cointegration among the variables, we observed a positive relationship between BTC and CARBON, a positive relationship between BTC and CPU, and a negative relationship between BTC and ENERGY. In terms of causal associations, we identified one-way causality running from CARBON to BTC, BTC to CPU, and BTC to the ENERGY variable. The study underscores the potential benefits and revenue opportunities for investors seeking diversified investment strategies in light of climate change concerns. Furthermore, it suggests actionable strategies for policymakers, such as implementing carbon taxes and educational campaigns, to foster a transition towards clean energy sources within the cryptocurrency mining sector and thereby mitigate environmental impacts.

Warming of the climate system is unequivocal, and many of the observed changes are unprecedented over five decades to millennia. Globally the atmosphere and ocean is increasingly getting warmer, the amount of ice on the earth is decreasing over the oceans, and the sea level has risen. According to Intergovernmental Panel on Climate Change, the total increasing temperature globally averaged combined land and surface between the average of the 1850-1900 period and the 2003 to 2012 period is 0.78 °C (0.72 to 0.85). But should we prepare for such the relatively small change? The importance is not the mean of the warming but the considerable likelihood of climate change that could trigger extreme natural hazards. The impact and the risk of climate change associated with railway infrastructure have not been fully addressed in the literature due to the difference in local environmental parameters. On the other hand, the current railway network in Malaysia, over the last decade, has been significantly affected by severe weather conditions such as rainfall, lightning, wind and very high temperatures. Our research findings point out the extremes that can lead to asset system failure, degraded operation and ultimately, delays to train services. During the period of flood, the embankment of the track can be swept away and bridge can be demolished, while during drought, the embankment of the track can suffer from soil desiccation and embankment deterioration, high temperature increases the risk of track buckling and high winds can result in vegetation or foreign object incursion on to the infrastructure as well as additional quasi-static burden exerted. This review is of significant importance for planning and design of the newly proposed high speed rail link between Malaysia and Singapore.

The migratory life history of anadromous salmonids requires successful migration between nursery, feeding and spawning habitats. Smoltification is the major transformation anadromous salmonids undergo before migration to feeding areas and prepares juvenile fish for downstream migration and entry to seawater. We reviewed the effects of climate change on smolt ecology from growth of juveniles in freshwater to early post-smolts in sea. Shift in the suitable thermal conditions by climate change is causing Atlantic salmon to expand their range northwards, while in the southern edge of their distribution populations struggle with high temperatures and occasional droughts. Climatic conditions, particularly warmer temperatures, are affecting growth during freshwater phase in the river. Better growth in the northern latitudes leads to earlier smoltification. Thermal refuges, the areas of cooler water in the river, are especially important for salmonids impacted by climate change. Restoring and maintaining connectivity and suitable diverse mosaic habitat in rivers are important for survival and growth throughout the range. The start of the smolt migration has shifted earlier as a response to increasing water temperatures, which has led to concerns of mismatch with optimal conditions for post-smolts in the sea decreasing their survival. A wide smolt window allowing all migrating phenotypes from early to late migrant’s safe access to sea is important in changing environmental conditions. This is true also for regulated rivers, where flow regulation practices cause selection pressures on migrating salmonid phenotypes. Life history in freshwater affects also marine survival, and there is a need for better collaboration across life stages and habitats among researchers and managers to boost the smolt production in rivers.

This paper identifies the characteristics of the farmer that affect the degree of farmer’s perceived-impact of climate change (CC). We use data from the Indonesian Rice Farm Household survey consisting of 87,330 farmers. An ordered probit regression model was used to estimate the effect of each variable on the degree of perceived-impact of CC. The results of this study confirm the previous empirical studies. Several variables that have been identified as having a positive effect on farmer adaptation practices such as farmer education, land tenure, irrigation infrastructure, cropping system, chemical fertilizer application, access to extension services and participation in farmer group affect the degree of CC perceived-impact negatively. However, a different result was found in the estimation of the gender variable. We found that female farmer has a higher resilience toward CC than the male farmer does. Furthermore, the female farmer has a more positive perception about future farming conditions than the male does. Finally, we suggest that the implementation of national adaptation policy should prioritize more to the farmer with insecure land tenure and utilize and expand the channel of agricultural extension services to deliver the planned adaptation policy.

This paper examines the interplay between media and the “social side of soils” whereby climate change action implicates individual and collective capacity to adapt, participate in groups or organizations, networks, and respond to challenges and opportunities across a system. The context examined here is Canadian agriculture, and soil health initiatives in the province of Ontario. Our review of relevant literature points to a knowledge gap on the role of the media in enabling climate action in the Canadian agricultural sector with a focus on soil management practices. This study conducted a media content analysis of 100 English-language news articles published between 2022 and 2024 and conducted 31 surveys with media professionals. Approximately one-quarter of the screened news articles contained any relevant coverage of soil health-related climate change mitigation issues. Journalist surveys identified the resource constraints on soil health media coverage with a wide range of traditional and Internet-based journalism on climate change issues, motivated particularly by crisis communication and one-off “parachute reporting”. Going forward, the engagement of key stakeholders of soil health in Canada such as farmers for media and communication about climate change mitigation needs attention. Key policy structures at the federal and provincial levels can help to make this happen.

Climate change is a global issue that draws widespread attention from the international society. As an important component of the climate system, the water cycle is directly affected by climate change. Thus, it is very important to study the influences of climate change on the basin water cycle with respect to maintenance of healthy rivers, sustainable use of water resources, and sustainable socioeconomic development in the basin. In this study, by assessing the suitability of multiple General Circulation Models (GCMs) recommended by the Intergovernmental Panel on Climate Change, Statistical Downscaling Model (SDSM) and Automated Statistical Downscaling model (ASD) were used to generate future climate change scenarios. These were then used to drive distributed hydrologic models (Variable Infiltration Capacity, Soil and Water Assessment Tool) for hydrological simulation of the Yangtze River and Yellow River basins, thereby quantifying the effects of climate change on the basin water cycle. The results showed that suitability assessment adopted in this study could effectively reduce the uncertainty of GCMs, and that statistical downscaling was able to greatly improve precipitation and temperature outputs in global climate mode. Compared to a baseline period (1961–1990), projected future periods (2046–2065 and 2081–2100) had a slightly decreasing tendency of runoff in the lower reaches of the Yangtze River basin. In particular, a significant increase in runoff was observed during flood seasons in the southeast part. However, runoff of the upper Yellow River basin decreased continuously. The results provide a reference for studying climate change in major river basins of China.

Crop productivity is directly dependent to soil fertility. High soil organic carbon (SOC) content in soil is vital as it leads to improved soil quality, increased productivity, and stable soil-aggregates. In addition, with the signing of the climate agreement, there is growing interest in carbon sequestration in landscapes. This paper looks at how SOC can be increased so that it not only contributes to reduction of CO₂, but also translates to increased food production thereby enhancing food security. This synergy between mitigation and enhancing food security is even more relevant for mountain landscapes of the Hindu Kush Himalayan (HKH) region where there remains huge potential to increase CO₂ sequestration and simultaneously address food security in the chronic food deficit villages. Soil samples were collected from seven transects each in Bajhang and Mustang and from 4 land use types in each transect. Samples of soils were taken from two depths in each plot; 0-15 cm below the soil surface and 15-30 cm below the soil surface to compare the top soil and subsoil dynamics of the soil nutrients. The lab analysis was performed to assess the soil texture, soil color, soil acidity in 'power of hydrogen' (pH), macro-nutrients as soil fertility. Secondary data was used to analyze the level of food deficit in the villages. The result shows that most of the sample soils from Mustang were clay (82.1%) which is 46 samples out of 56. The pH value of soil from Bajhang ranged from 5.29 to 9.09. The pH value of soil ranged from 5.65 to 8.81 in Mustang. SOC contents of sampled soils from Bajhang ranged from 0.20% to 7.69% with mean amount of 2.47% ± 0.17. SOC contents of sampled soils from Mustang ranged from 0.51% to 8.56% with mean amount of 2.60% ± 0.25. By land use type, forest land had the highest carbon (C) content of 53.61 t ha^-1 in Bajhang whereas in Mustang, agricultural land had the highest C content of 52.02 tons ha-1. Based on these data, we can say that there is potential for increasing SOC through improved soil health and crop production and soil. Sustainable soil management should be practiced for higher productivity. Livestock may also provide farmyard manure, which can be used to fertilize cultivated soils, which increases soil productivity. Increasing productivity would aid in increasing the access and availability of food in these mountain villages.

Daily maximum surface air temperature (Tamax) is a crucial factor for understanding complex land surface processes under rapid climate change. Remote detection of Tamax has widely relied on the empirical relationship between air temperature and land surface temperature (LST), a product derived from remote sensing. However, little is known about how such a relationship is affected by the high heterogeneity in landscapes and dynamics in seasonality. This study aims to advance our understanding of the roles of land cover and seasonal variation in the estimation of Tamax using the MODIS (Moderate Resolution Imaging Spectroradiometer) LST product. We developed statistical models to link Tamax and LST in the middle and lower reaches of the Yangtze River in China for five major land-cover types (i.e., forest, shrub, water, impervious surface, cropland, and grassland) and two seasons (i.e., growing season and non-growing season). Results show that the performance of modeling the Tamax-LST relationship was highly dependent on land cover and seasonal variation. Estimating Tamax over grasslands and water bodies achieved superior performance; while uncertainties were high over forested lands that contained extensive heterogeneity in species types, plant structure, and topography. We further found that all the land-cover specific models developed for the plant non-growing season outperformed the corresponding models developed for the growing season. Discrepancies in model performance mainly occurred in the vegetated areas (forest, cropland, and shrub), suggesting an important role of plant phenology in defining the statistical relationship between Tamax and LST. For impervious surfaces, the challenge of capturing the high spatial heterogeneity in urban settings using the low-resolution MODIS data made Tamax estimation a difficult task, which was especially true in the growing season.

The aim of this study was to develop a valid and reliable questionnaire to assess vitamin D-related knowledge, attitude and practice (KAP) in Iranian adults who may be at increased risk of vitamin D deficiency. This study was conducted on 527 subjects, aged ≥20 years from Public health care centers in Tehran, Iran. Based on results of literature review and in-depth interviews, the 38-item vitamin D-related KAP questionnaire with four subscale was developed: 1) general knowledge; 2) nutritional knowledge; 3) attitudes; 4) behaviors. Validity of the developed vitamin D-KAP questionnaire was assessed, utilizing face, content, and construct validity methods. Internal consistency was calculated to assess reliability of the current developed questionnaire. A total of 572 (54.1% female) adults, aged 30.2±7.9 years, participated in the study. All items were perceived as relevant and comprehendible by participants. Content validity was confirmed by the panel of experts. The internal consistency, as measured by Cronbach’s alpha coefficients, exceeded the minimum reliability standard of 0.60 for four subscales. The EFA suggested a four-factor construct and the results of the CFA indicated acceptable fit indices for the proposed model. No ceiling effects were observed except for general knowledge (1.2%). Floor effects detected were 0, 1.1, 2.4, and 8.7% for practice, attitude, general knowledge, and nutrition knowledge, respectively. General knowledge had the highest score (79.59±14.52) and nutrition knowledge, the lowest score (42.58±20.40) among the four sub-scales. Results confirm the initial validity and reliability of the vitamin D-related knowledge, attitude and practice questionnaire. Further investigations in different urban and rural population are recommended.

Myanmar is located in the Indo-Burma biodiversity hotspot, but lacks understanding of the bio-diversity hotspots and protected areas within its borders. Climate change affects the effective-ness of protected areas and the potential distribution range of species. Based on Myanmar's re-gional characteristics, endangered status, and representativeness, this paper selects species and identifies biodiversity hotspots using the MaxEnt model. We studied the biodiversity within the terrestrial protected area network in Myanmar, as well as the potential for cross-boundary range shifts among protected areas under different climate scenarios. Species populations outside My-anmar's protected area network may not be able to find suitable habitats in the future, further exacerbating climate-related extinction risks. Additionally, some protected areas in Myanmar may not be fully utilizing their biodiversity representation potential, and with scientific spatial planning, Myanmar's protected areas could support a large number of species. In the context of continued warming, identifying new protected areas that encompass the future and current climatic niches of protected areas can increase the capacity for sustained biodiversity conservation. We hope that this study provides insights for increasing the effectiveness of Myanmar's protect-ed area network and emphasizes the urgency of coordinated protection of protected areas and biodiversity hotspots.

Nanotechnology has become a popular topic in agricultural and animal research, generating sig-nificant global interest. Nanoparticles (NPs)-enabled agriculture and animal husbandry (AHs) products have three main principles: crop production, protection, and nutrient enrichment. Re-search on this topic has grown exponentially in recent years, with an increasing number of peer-reviewed literature and patents on NPs-enabled agriculture and AH products. This study examines and collects data on the various applications of NPs-enabled agricultural products for crop production, protection, and livestock improvement from the core nanotechnological database. The search returned several types of NPs and products used in agriculture and AH, countries that utilize these products at a commercial level, and analysis of these data by phyton, R-statistical software. Our findings show remarkable growth of nanotechnology in agriculture, AH, and re-search fields for future sustainability goals in the era of climate change

In the era of persistent globalization, climate governance has emerged as a prominent concern within both the theoretical community and government departments of diverse nations. Of particular interest in academic research is the adverse effect of climate shocks on the global economy. This paper employs average temperature as a surrogate indicator for climate shocks and examines the influence of temperature fluctuations on inflation levels using a balanced panel dataset from 1995 to 2021. The findings indicate a positive association between temperature change and inflation within the country, which remains consistent even after subjecting the analysis to multiple robustness tests. Furthermore, accounting for heterogeneity reveals variations in the magnitude of response of inflation levels to temperature fluctuations. Regarding the analysis of underlying mechanisms, the study underscores the significance of energy demand as a pivotal pathway influencing inflationary pressures at the national level. Lastly, by incorporating GDP per capita as a threshold, the research reveals a nonlinear relationship between temperature change and inflation levels.

The climate of Bangladesh has changed drastically which may put considerable adverse impacts on mangrove fishers but very few studies focused on this professional group. An attempt was made to perceive the impact and adaptation measures of the Sundarbans mangrove resource users, employing interviews and focus group discussions. A total of 150 respondents were randomly selected from the Sundarbans west under Shyamnagar Upazila of Satkhira District. It was revealed that the abundance of fishes, fuel woods, honey, golpata (Nypa fruticans), and shrimp post-larvae (PL) was reduced considerably. The resource users have adapted themselves by changing their occupation and becoming jobless and depending on the other family members. PL collection, honey collection, shrimp culture, and wood collection were found professional adapting strategies to adopt cyclone, flood, salinity intrusion, river erosion, and drought. Several recommendations are elicited, the implementation of which is important to ensure livelihood sustainability of the mangrove communities.

The present study aims to measure the technical efficiency and establish core factors effecting rice production in Cambodia. Four-years dataset generated from the central government document “Profile on Economics and Social” of entire 25 provinces between 2012-2015 and the stochastic production frontier model (SFA) was applied. The results indicated that the level of output (quantity) of Cambodian rice production varied according to the different level of capital investment in agricultural machineries, total rice actual harvested area, and technically fertilizers application within provinces. Furthermore, evidence revealed the overall mean efficiency of rice production is 78.4% implies that there is still room to further improve technical efficiency by given the same level of inputs and technology. More importantly, the findings revealed that irrigation, production technique and amount of agricultural supporting staffs are being as the most important influencing factors of rice production’s technical efficiency in Cambodia. In conclusion, present study strongly recommends the development of irrigation systems and good water management practices to be considered and bring into more effective actions by the central government as well as related agencies for improving rice production in Cambodia in addition to capital investment and improving technical skills of supporting staffs and rural farmers.

P-band radar remote sensing applied during the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission has shown great potential for estimation of root zone soil moisture. When retrieving the soil moisture profile (SMP) from P-band radar, a mathematical function describing the vertical moisture distribution is required. Because only a limited number of observations are available, the number of free parameters of the mathematical model must not exceed the number of observed data. For example, a second order polynomial that contains 3 free parameters was presumed based on in-situ SMP data. The polynomial is currently parameterized based on 3 backscatter observations provided by AirMOSS (i.e. one frequency at three polarizations of HH, VV and HV). In this paper, a more realistic, physically-based SMP model containing 3 free parameters is derived based on a solution to Richards’ equation for unsaturated flow in soils. Evaluation of the new SMP model based on both numerical simulations and measured data revealed that it exhibits greater flexibility for fitting measured and simulated SMPs than the currently applied polynomial. It is also demonstrated that the new SMP model can be reduced to a second order polynomial at the expense of fitting accuracy.

In the process of climate change mitigation and adaptation, the focus on climate risk driven by transition innovations has received widespread attention from governments, regulators, and investors. Based on the identification of low-carbon patents through the Green and Low-carbon Technology Inventory in China, this study constructs low-carbon innovation measurements of listed firms in China from 2015 to 2021. Then, an empirical model is used to investigate the impact and mechanism of climate transition innovations on the default risk. The baseline regression model shows that low-carbon innovations can mitigate the default risk of listed firms. Moreover, our findings are supported by instrumental variable regressions that use the time costs of innovation. Finally, the mediation effect demonstrates that investor attention, total factor productivity, and technology spillovers are paths through which transition innovations can affect the default risk. This study reveals that low-carbon technological advances have a positive effect on climate transition risks.

The current study was planned to simulate runoff due to the snowmelt in the Lidder River catchment of Himalayan region under climate change scenarios. A basic degree-day model, Snowmelt-Runoff Model (SRM) was utilized to assess the hydrological consequences of change in climate. The SRM model performance during the calibration and validation was assessed using volume difference (Dv) and coefficient of determination (R2). The Dv was found as 11.7, -10.1, -11.8, 1.96, and 8.6 during 2009-2014, respectively, while the R2 is 0.96, 0.92, 0.95, 0.90, and 0.94, respectively. The Dv and R2 values indicating that the simulated snowmelt runoff has a close agreement with the observed value. The simulated findings were also assessed under the different scenarios of climate change: a) increases in precipitation by +20 %, b) temperature rise of +2 °C, and c) temperature rise of +2 °C with a 20 % increase in snow cover. In scenario "b", the simulated results showed that runoff increased by 53 % in summer (April–September). In contrast, the projected increased discharge for scenarios "a" and "c" was 37 % and 67 %, respectively. In high elevation data-scarce mountain environments, the SRM is efficient in forecasting future water supplies due to the snowmelt runoff.

Climate change represents a major threat to lotic freshwater ecosystems and their ability to support the provision of ecosystem services. England’s chalk streams are in a poor state of health, with significant concerns regarding their resilience, the ability to adapt, under a changing climate. This paper aims to quantify the effect of climate change on hydroecological response, the health of the river, for the River Nar, a SSSI in the south-east of England. To this end, we apply a coupled hydrological and hydroecological modelling framework, with the UKCP09 probabilistic climate projections serving as input (A1B high emissions scenario). Results show that, from 2021 to the end of the century, hydroecological response becomes more heterogeneous. Despite the limited range of the functional feeding groups on the baseline, the River Nar has been able to adapt to extreme events due to inter-annual variation. In the future, this variation is greatly reduced, raising real concerns over the resilience of the river ecosystem under climate change. These new insights into the health of the River Nar, and chalk streams more generally, highlights the necessity of further study and the real need to for changed river management practices.

According to the IPCC guidelines, CO₂ emissions from biomass should be excluded from the entire amount of CO₂ emissions when calculating CO₂ emissions and should be separately reported due to the “carbon neutrality.” Sewage sludge is one of the representative biomass fuels. And it is mixed with fossil fuels in terms of greenhouse gas reduction or is used as fuel to replace fossil fuels by itself. According to the results of this study, biomass contents of both the sewage sludge and the sewage sludge incineration exhaust gases did not amount to 100%. At present, in many countries(South Korea, Japan, and Germany), when calculating greenhouse gas emissions from sewage sludge incinerator, all CO₂ emissions from sewage sludge are judged to be biomass and only those greenhouse gas emissions that correspond to Non-CO₂ gases are calculated as greenhouse gas emissions. However, since, according our results, the content of sewage sludge is not 100% biomass, if CO₂ emissions are excluded according to the existing greenhouse gas emission calculation method, the amount of emissions may be underestimated. Therefore, to accurately calculate greenhouse gas emissions from sewage sludge incinerator, CO₂ emissions should be calculated in consideration of the fossil carbon contents of sewage sludge.

Despite ample warning and significant lead time, global society has not acted sufficiently to combat climate change, and global carbon dioxide emissions are now about 60% higher today than in 1990 [1]. This paper uses marine fisheries as an analog model to compare with the many similar management issues faced with global climate change. Both marine fisheries and global climate change are complex systems that have similar properties that make management very difficult. A simple fishery prototype model is used to educate citizens about similar issues of dynamic complexity found in climate change models. We highlight how our slow reaction time to global climate change can be thought of as a mismatch between our cognitive abilities and the nature of global collective problems. Understanding the history and system structure of marine fisheries can provide insight into managing global climate change. We suggest ways to improve communication between science and citizens for updating mental models and changing climate policy.

In this study, I extend the Fama and French five-factor asset pricing model with a sixth factor, namely, carbon risk, to investigate its impact on equity returns. To measure carbon risk, a new factor ‘pollutant minus green,’ is developed using the difference between the weighted average returns of pollutant and green firms across 51 developed and emerging countries across four categories—North America, Europe, Emerging Markets, and the Asia Pacific. The results reveal that North America, Europe, and Asia Pacific markets have a carbon risk premium that gets eliminated in small-cap firms. The carbon risk factor is further tested in left-hand side (LHS) test asset portfolios and found to be more pronounced with size-effect anomaly; specifically, small stock firms report greater declining average returns because of more exposure than the mega-cap stocks to carbon dioxide emissions. Furthermore, size-effect anomaly prevails with profitability and investment factors across firms. Therefore, high profitability, as well as high investment small firms, show a greater decline than the big stock firms in average returns when their carbon dioxide emissions increase. The asset pricing model evaluation is carried out through the Gibbons, Ross, and Shanken test. The six-factor model directed at capturing carbon risk patterns in average equity returns performs better than the three-factor and five-factor models of Fama and French (1993 and 2015) in the majority of categories under 3x3 sorting and compete with both Fama and French model under 2x4x4 sorted LHS portfolios. The finding of this study offers various useful applications for investors, policymakers, brokers, corporations, governmental pollution abatement institutions, and other stakeholders who wish to obtain carbon risk premium.

Anthropogenic activities are the main cause of climate change globally. The World Health Organization (WHO) recognized that heating and precipitation are the reason for climate change due to anthropogenic actions over the past 30 years and claim 150,000 lives annually. Climate change is also affecting the atmospheres of polar regions. Numerous diseases are linked with climate change like cardiovascular mortality and respiratory illnesses. Due to climate change, lack of long-term and high-quality data sets, variations in drug resistance and immunity, the amplification or the resurgence of diseases, as well as many socio-economic factors, are uncertain.Potentially susceptible regions consist of the temperate latitudes, the regions near the Indian and Pacific oceans are affected due to the heavy rainfall where the heat of the cities could increase life-threatening climatic proceedings. The association between climate and health poses a risk to health under future guesses. Over the current decades, warming has contributed to increased mortality and morbidity in various provinces of the globe. The current study reviews the potential effects of climate change on temperature, human health, air quality, food & nutrition, livelihoods, and livestock & fisheries in detail.

The Energy Performance of Buildings Directive 2010 calls for the Nearly Zero Energy Standard for new buildings from 2021 onwards: Buildings using “almost no energy” are powered by renewable sources or energy produced by the building itself. For residential buildings, this ambitious new standard has already been reached. But for other building types this goal is still far away. The potential of these buildings to meet a Nearly Zero Energy Standard was investigated by analyzing ten case studies representing non-residential buildings with different uses. The analysis shows that the primary characteristics common to critical building types are a dense building context with a very high degree of technical installation (such as hospital, research and laboratory buildings). The large primary energy demand of these types of buildings cannot be compensated by building and property-related energy generation including off-site renewables. If the future Nearly Zero Energy Standard were to be defined with lower requirements because of this, the state related properties of Bavaria suggest that the real potential energy savings available in at least 85% of all new buildings would be insufficiently exploited. Therefore, it would be useful to instead individualize the legal energy verification process for new buildings to distinguish critical building types such as laboratories and hospitals.

Bettelmatt cheese, a cherished Italian culinary gem hailing from the picturesque Val d'Ossola region, is deeply intertwined with its natural environment. This article explores the intricate relationship between the organoleptic attributes of Bettelmatt cheese and the challenges posed by climate change. The sensory qualities of this cheese, including its flavor profile, texture, and aroma, are profoundly influenced by the alpine pastures where the cows graze, the unique terroir, and the traditional production methods. However, as climate change continues to impact ecosystems and weather patterns, it raises questions about the resilience and adaptability of this traditional cheese. In addition to these challenges, the article would like to explore the complexities of regulation and protection of this cheese, crucial for food and gastronomic sciences.

The bioindicator potential of seagrass ecosystems in coastal trace metal monitoring is low for tropical island ecosystems. This study evaluated the bioindicator potential of six seagrass species exposed to anthropogenic trace metal pollution in the Andaman and Nicobar Islands (ANI) of India. Sediment and seagrass biomass, samples were analyzed for trace metals (Co, Cr, Cu, Fe, Mg, Mn, Ni, Pb, Zn) from four locations of ANI exposed to anthropogenic contamination. Geo accumulation Index values indicated moderate trace metal contamination in seagrass sediment. Seagrass trace metal accumulation from sediment was both species-specific and location- specific for ANI. Small seagrass species such as H. ovalis, H. beccarii and H. uninervis accumulated the maximum concentration of six trace metals (Cr, Cu, Fe, Ni, Pb, Zn) in their tissues compared to big seagrass species like T. hemprichii and E. acoroides of ANI. This study indicates leaves of small seagrass and roots of big seagrass species can serve as short- and long-term bioindicators respectively, of coastal trace metal contamination in tropical islands of ANI.

Agriculture contributes towards South African economy, but this sector is highly vulnerable to risks imposed by climate change. Smallholder maize farmers are specifically susceptible to climate change impacts. Maize crop plays a crucial role in the country's food security as it is considered a stable food for households and source of feed for livestock. The study aimed to analyze the socio-economic factors influencing smallholder maize farmers’ willingness to adopt climate-smart agriculture. It was conducted in three different areas due to their specific agro-ecological zones. Multipurpose research design was used to gather data, and multistage random sampling to choose the study areas. Subsequently, 209 purposively selected farmers were interviewed face-to-face using structured questionnaires and focus discussion groups. Descriptive statistics and Double-hurdle regression model were used to analyze the data. Descriptive results indicated that 81%, 67%, and 63% farmers were willing to adopt CSA in Ga-Makanye, Gabaza, and Giyani, respectively. Empirical results from the first and second hurdles of the regression analysis showed that farmers’ education, crop diversification, information about CSA positively influenced the decision to adopt CSA while household size and agricultural experience negatively influenced the decision to adopt CSA. It is recommended that the Department of Agriculture, Land Reform, and Rural Development should provide CSA workshops and educational programs to farmers to enhance their knowledge and decision-making processes regarding adaptation strategies.

Abstract: A large number of collecting expeditions were launched in regions of ‘centers of diversity’ and hundreds of thousands of sample have been collected and stored in gene banks as ‘genetic resources’. So far, only a small number of the samples have been evaluated for their biotic and abiotic stress tolerance. Now, their time to become useful has come. A new global phenomenon has arisen – climate change. The crop genetic resources and their wild progenitors that have survived countless years of changing environment during the last 11,000 years could harbor genes that may be useful under the new growing conditions and environmental factors thrown up by climate change and global warming. With the deployment of modern bio-engineering techniques selected genes or gene fragments can be transferred from genetic resources to modern varieties of crop plants to make them well-prepared to mitigate the effects of global warming and climate change. The latter is the most serious issue facing plant breeders today. New pests and diseases could affect crop production. These review paper discusses various impacts and issues as a result of this phenomenon and suggest ways to safeguard our most important crops through better management of crop plant genetic resources in the near future.

Recent research emphasises the importance of Artificial Intelligence applications as supporting tools for students in higher education. Simultaneously, an intensive exchange of views has started in the public debate in the international educational community. However, for a more proper use of these applications, it is necessary to investigate the factors that explain their intention and actual use in the future. With the Unified Theory of Acceptance and Use of Technology (UTAUT2) model, this work analyses the factors influencing students' use and intention to use artificial intelligence technology. For this purpose, a sample of 199 Greek students at the School of Humanities and Social Sciences from the University of Patras participated in a survey. The findings highlight that expected performance, habit, and enjoyment of these Artificial Intelligence applications are key determinants influencing teachers' intentions to use them. Moreover, behavioural intention, habit, and facilitating conditions explain the usage of these Artificial Intelligence applications. The study did not reveal any moderating effects. The limitations, practical implications, and proposed directions for future research based on these results are discussed.

In this study the effects of three different main preparatory tillage operations [ploughing at 0.4 m (P40) and 0.20 m (P20) depth and minimum tillage at 0.20 m depth (MT) each of them carried out at two different soil water contents (WC) [low, 58% (LH) and high, 80% (HH) of field capacity] were investigated. The results obtained in this research show high values of soil strength in term of Penetration resistance (CI) and shear strength (SS) particularly in deeper soil layers at lower water content. Fossil-fuel energy requirements both for P40 LH and P20 LH were 25 and 35% higher with respect to the HH treatments and tractor slip were very high (P40 LH = 32.4%) with respect to the P40 HH treatment (16%). Therefore soil water content had significantly influenced tractor performance during soil ploughing, particularly at 0.40 m depth while MT was not influenced at all. A significant correlation between grain yield and soil penetration resistance was found highlighting how soil strength may be good indicator of its productivity. Obtained results during these field tests allowed considering MT and P20 treatments more suitable for this type of soil in climate change scenarios.

This paper explores the effect of climate change on the regression of the Argan tree (Argania spinosa L. Skeels) woodland, focusing on the Argan Biosphere Reserve and especially in the Souss plain (Western Morocco). Rainfall and temperature data of four sites within the Argan Biosphere Reserve were analyzed over the last 60 years to assess any climatic change. Regression curves applied to the dataset showed an important decrease in rainfall (18 to 26 %) in the four locations as well as an increase in temperature (1 to 2 °C). These changes may have a detrimental effect on the Argan woodland although human factors have been reported to be the main factor of its regression. It can therefore be concluded that the reduction in rainfall and the increase in temperature should now be considered as factors of Argan woodland regression.

This study investigates the impacts of climate change policies on businesses across various sectors, aiming to understand how these regulations affect operational, financial, and strategic aspects of business management. Utilizing a mixed-methods approach, data was collected from 500 businesses through surveys and interviews, encompassing a wide range of industries, sizes, and geographic locations. The findings reveal a complex landscape where 30% of businesses report positive impacts, primarily in the renewable energy and technology sectors, citing increased demand for green products and improvements in energy efficiency. Conversely, 45% of businesses, especially from manufacturing and agriculture, experience negative impacts, including increased operational costs and regulatory compliance challenges. Notwithstanding, a significant majority of businesses are committed to enhancing their sustainability practices, viewing climate change policies as an opportunity for innovation and long-term growth. The study highlights the nuanced effects of climate change policies, emphasizing the need for sector-specific support measures to alleviate financial and operational burdens on negatively impacted businesses. Moreover, it points to a broader shift towards sustainability within the business community, suggesting that climate policies, despite their varied impacts, are driving significant changes in business strategies towards environmental stewardship.

Climate change is a big challenge for agriculture since it affects crop productivity and yield. The increase of droughts, salinity, and soil degradation are some of the main consequences of climate change. The use of microorganisms has emerged as an alternative to mitigate them. Among these microorganisms, dark septate endophytes (DSEs) have garnered more attention in recent years. Various studies show that their association with plants helps reduce the harmful effects of abiotic stresses and increases nutrient availability, allowing plants to thrive under adverse conditions. This work reviews the effect of DSEs and the subjacent mechanisms that will help plants develop a higher tolerance to climate change.

Wildfire is the main disturbance in forested ecosystems of southern Europe and is due to complex interactions between climate-weather, fuels and people. Warmer and drier conditions projected in this region are expected to profoundly affect wildfires, which will impact ecosystems and humans. We review the scientific literature addressing the assessment of climate change impacts on wildfires in southern Europe, with a twofold objective: (i) report the trends in wildfire danger and activity projected under warming climate in southern Europe and (ii) discuss the limitations of wildfire projections under the specific biogeographical context of southern Europe.We identified 22 projection studies that examined future wildfire danger or wildfire activity at local, regional or continental scale. Under the scenario with the highest greenhouse gas emissions, we found that projections studies estimate an increase in future fire danger and burnt areas varying, on average, from 2 to 4 % and from 15 to 25 % per decade, respectively. Fire-prone area expansion to the north and to Mediterranean mountains is a concern, while climate-induced burnt area increase might be limited by fuel availability in the most arid areas. While all studies agreed on the direction of changes, further comparisons on the magnitude of increase remained challenging because of heterogeneous methodological choices between projections studies (climate models, projection period, spatial scale and fire metrics). We then described three main sources of uncertainty that may affect the reliability of wildfire projections: climate projections, climate-fire models, and the influences of fuel load/structure and human related factors on the climate-fire relationships. We finally suggest research directions to address some of these issues for the purpose of refining fire danger and fire activity projections in southern Europe.

Panel data of our interest consist of a moderate number of panels, while the panels contain a small number of observations. An estimator of common breaks in panel means without a boundary issue for this kind of scenario is proposed. In particular, the novel estimator is able to detect a common break point even when the change happens immediately after the first time point or just before the last observation period. Another advantage of the elaborated change point estimator is that it results in the last observation in situations with no structural breaks. The consistency of the change point estimator in panel data is established. The results are illustrated through a simulation study. As a by-product of the developed estimation technique, a theoretical utilization for correlation structure estimation, hypothesis testing, and bootstrapping in panel data is demonstrated. A practical application to non-life insurance is presented as well.

Changes in the dates of phenological phases of plants and insects reflect changes in climate. The aim of the study was to determine the phenological patterns and interrelationships of spring-flowering entomophilous plants (Corylus avellana L., Alnus incana Moench., Tussilago farfara L., Salix caprea L., Acer platanoides L., Taraxacum officinale L., Prunus L., Malus domestica Mill.) and the honey bee (Apis mellifera L.) in response to climate change. The research was carried out at Vokė Branch of the Institute of Agriculture, Lithuanian Research Centre for Agriculture and Forestry, using data from phenological observations during 1961–2020. The results of the studies showed that over a 60-year period, a trend towards earlier dates of all phenological events studied was observed. Significant and larger-scale changes occurred starting from the decade 1981–1990. Throughout the entire study period, with the exception of one decade, the dates of honeybee emergence on flowers correlated reliably with the dates of entomophilous plant phenophases. Due to the advance of plants phenophase dates the synchrony with honeybee emergence dates changes however, these changes had a positive effect on the foraging conditions of overwintering honeybees.

In this study, we develop a stochastic partial differential equation (SPDE) model to assess the regional severity of temperature increases due to global warming. The model incorporates various stochastic processes to account for uncertainties such as solar explosions, economic upheavals, rising emissions, and irreversible changes in the climate system. Our base temperature model, influenced by latitude, is enhanced by these random factors to simulate realistic and diverse outcomes of climate change. Numerical simulations on a global grid provide a heatmap representing potential temperature increase severity across different regions. The results highlight the significant impact on tropical and polar regions, demonstrating the utility of SPDE models in understanding and predicting the complex dynamics of climate change. This approach offers valuable insights for policymakers and researchers aiming to develop adaptive strategies for mitigating the adverse effects of global warming.

The effect of climate change plays a significant role on groundwater level variations and crop yield. The change in climate leading to increased temperatures, decreased rainfalls and extreme drought conditions ultimately cause low groundwater level and crop yield. Judicious groundwater management technology viz., water recharge options, need based irrigation, crop selection, must be adapted for further increasing the water table level in soil ecosystem. Additionally, climate change impacts combined with changes in agricultural water use can affect groundwater dynamics. Increased irrigation demand and decreased summer precipitation can lower groundwater levels, impacting crop production. Overall, climate change affects both groundwater resources and crop yield highlighting the need for sustainable water management practices and consideration of soil properties in agricultural modelling. The authors carefully selected relevant research articles addressing the impact of climate change on ground water level in Asian countries. The review highlights the use of machine learning method to ground water level and crop yield modelling. According to the study, machine learning techniques have made significant contributions to predicting groundwater level changes and crop yield with higher accuracies, high performance and less running time.

In the paper, the authors find closed forms for derangement numbers in terms of the Hessenberg determinants, discover a recurrence relation of derangement numbers, present a formula for any higher order derivative of the exponential generating function of derangement numbers, and compute some related Hessenberg and tridiagonal determinants.

Flooding and overflows are recurring problems in several Brazilian cities, which usually undergo disorderly development. Their causes vary from increased impervious surface areas, deficiency/inefficiency of drainage structures and their maintenance, siltation of rivers, channel obstructions, and climatic factors. This situation is aggravated in the major cities. The Anhangabau watershed lies in the central portion of the city of Sao Paulo – Brazil and covers a drainage area of 5.4 km². The region is highly urbanized and crossed by a major north-south road connection. During heavy rain events, portions of this interconnection passage become compromised, disrupting the flow of vehicles, creating a chaotic situation for the population, as well as losses to the national economy. Observed rainfall records and an existing IDF (intensity duration frequency) curve for the region are used to obtain design storms. To account for climate change, a well know procedure, the equidistance quantile matching method for updating IDF curves under climate change, was applied to the existing historical data. Several different global climate models (GCM) and one regional model were applied to obtain and update rainfall design storm. The GCMs and future scenarios used were from the IPCC Assessment Report 5 (AR5) and two future projections: RCP (representative concentration pathway) 4.5 and 8.5. Alternatives previously proposed to solve to flooding issue are briefly reviewed. On one of the latest studies [1], a few modern concepts of water resources management are presented, and the linear retention measure was found to offer higher potential to mitigate the flooding problem in the lower valley of the watershed. Therefore, this alternative was used to evaluate different design storms scenarios combined with return periods of 25 and 100-years as well as the updated IDF under climate change for RCP 4.5 and RCP 8.5. To model the complex network, representing both road and drainage systems and their interconnections, PCSWMM/SWMM software was applied. Results are presented as flooding maps and show the impacts of the proposed linear retention measure based on the existing IDF curves and the updated IDF curves under climate change for two different drainage system conditions, current and improved with the use of linear retention reservoirs. Results show that the prosed changes on the drainage system help reduce the risk and damage to flooding. The climate change scenarios, however, impose a significant threat and need immediate attention from city planners and stakeholders.

Abstract: Doppler renal resistive index (RRI) has been used for several decades as a potential indi-cator for renal disease and progression. In recent years, RRI has emerged as a useful prognostic indicator for a range of co-morbidities in patients who are acutely unwell and as a potential marker for sub-clinical acute kidney injury (AKI) and post procedural AKI risk. Despite this, the clinical and theoretical determinants of RRI are poorly understood and remain contentious. Method: A narrative review of studies exploring the theoretical, experimental, and clinical evidence for factors affecting RRI. No date restrictions were set. Findings: Both experimental and clinical data confirm that RRI is markedly affected by a range of systemic and renal determinants and is influenced closely by the underlying risk factors and sys-temic vascular changes that lead to progressive renal damage. Discussion: This paper provides an overview of the key haemodynamic determinants of renal re-sistive index and aims to clarify their relative importance. With reference to a simplified theoretical model, evidence from experimental and clinical studies is reviewed. Pathophysiological factors and patient characteristics that influence RRI are explored to provide a better understanding of why RRI values change in the presence of common or complex pathologies. Conclusion: By gaining a better understanding of the complex systemic and renal factors that influ-ence RRI, ultrasound users will be better prepared to understand and interpret changes in RRI across a range of patient presentations.

The study of precipitation amount at different time scales constitutes an important issue in climate research and risk assessment. When dealing with daily totals, a frequent but sometimes underestimated problem is at what time the observation day begins. The choice of different starting times may lead to incompatibility between stations and incorrect identification of extreme events. In this work, the problem of temporal misalignment between precipitation datasets characterized by different starting time of the observation day is analyzed. The most widely used adjustment methods (1day and uniform shift) and two methods based on reanalysis (NOAA and ERA5) are evaluated in terms of temporal alignment, precipitation statistics and percentile distributions. As test series, the precipitation amount collected from 9 a.m. local time (09 LT) on the previous day to 09 LT on the target day (9-9 datasets) of the Padua and nearby stations in the period 1993-2022 have been selected. Results show that the reanalysis-based methods, in particular ERA5, outperform the others in temporal alignment, regardless the station. But, for the periods in which reanalysis data are not available, “1day” method, which shifts the daily amount back one calendar day, and “unif” method, which distributes uniformly the daily total from a 2-day moving window surrounding the target date, can be considered valid alternatives. On the other side, concerning the precipitation statistics, the reanalysis-based methods are not the best option, as they increase the precipitation frequency and reduce the mean value over wet days, NOAA much more than ERA5. Nevertheless, the uniform method provides a larger deviation from the original daily series. The use of the series of a station nearby the target one, which is mandatory in case of missing data, gives similar or better results than applying any adjustment method to the 9-9 series. General conclusions can hardly be drawn as they depend on the method and station. For the Padua dataset, the analysis was repeated at monthly and seasonal resolution. In general, the adjustment series show the most relevant changes in the precipitation statistics in summer and less temporal alignment with the original series in summer and autumn, the two seasons mainly affected by heavy rains in Padua. Finally, the percentiles distribution, analyzed for all the methods and stations, indicates that any adjustment method underestimates the percentile values, except ERA5. Only Legnaro, the station most correlated with Padua, gives results like ERA5.

Abstract: Long-term rainfall, temperature and solar radiation time series data are required to simulate crop yield and yield variability. A field experiment conducted at Mount Makulu was used to simulate the interactive effect of planting dates (SD1, SD2, SD3), maize varieties (PIO30G19, PIO30B50, ZMS606), and nitrogen fertilizer application levels (N1 = 66; N2 = 132; N3 = 198 kg N ha-1) on strategic and economic assessment. Statistical downscaled climate datasets from three GCMs from 1971-2000, 2010-2039, 2040-2069, and 2070-2099 using Representative Concentration Pathways (RCP4.5, RCP8.5) were utilized as DSSAT v4.7 inputs. The Seasonal analysis Program of the DSSAT model was used to simulate the impacts of climate change on maize yield. Results show increasing trends in temperature while there is variability in rainfall. The biophysical analysis showed varied grain yield responses to sowing date, maize cultivars and N application rates. The Mean-Gini analysis showed that PIO30B50 had an efficient late sowing data (SD3) with an application of 132 and 168 kg N ha-1 under both scenarios. Further, PIO30G19 at SD3 with 198 kg N ha-1 would be the most dominant management option for maize grain yield under future climate scenarios from 2010-2099. This research emphasizes the urgency for tailored adaptation actions and collaborative efforts along the maize value chain to mitigate future yield losses and sustain food security. Increasing maize grain yield requires implementing adaptation strategies such as varying sowing dates, adopting late-maturing varieties with high thermal heat requirements under future climate scenarios.

Research Highlights: Hagenia abyssinica is geographically localized, poor regenerated and endangered species in Ethiopia. Ethiopia has been experiencing variability of rainfall and rise in temperature due to the climate change. This study has hypothesized that the suitable areas for the species will be narrowed by the year 2070. Background and Objective The prediction of species distribution models help to implement appropriate conservation actions. The aim of this research was to identify the current and likely future distribution range and suitable areas for the species, and to determine the presence of H. absyssinica in risk in a short-term future. Material and method: To this end, occurrence data, bioclim variables, soil, elevation, and land cover map of Ethiopia were used. MaxEnt was used to predict distribution. Climate change impacts on the distribution of the species was performed using bioclimatic variables of the future climate data, 2070 (average for 2061-2080) was obtained from IPPC5 (CMIP5) at 30 seconds (1km) spatial resolution. The climate data was projected from GCMs, downscaled and calibrated using rcp4.5. Results: Both current and likely future distribution models were excellent and significantly better than random performance. This study has computed 59987 km2 to be the low impact area for the species under current conditions and will remain habitat under future climates and 39025 km2 area has been identified as the possible high impact areas or declining habitat. The model has also determined that 1238724 km2 of the areas are unsuitable at present and for future climates. The current study found that 15751 km2 of the area will be modified as a new suitable area for H. abyssinica due to climate change. Conclusion: Species distribution modeling is essential for the implementation of conservation actions that are compatible with the inevitable changing climatic conditions of the country.

The Antarctica and High Arctic regions are extreme environments, with average maximum temperatures below 0 °C for most days of the year. Interestingly, fungi inhabit these regions. This review describes the history of fungal surveys near the Syowa Station and the fungal diversity in this region. In the High Arctic region, I summarized the changes in fungal communities in glacial retreat areas of Ny-Ålesund, Norway and Ellesmere Island, Canada in response to climate change. In addition, growth and enzyme secretion ability of Antarctic and Arctic fungi at sub-zero temperatures are presented. Finally, I have also provided the future directions of Antarctic and Arctic fungal research.

Climate change in the coming decades could intensify extreme events such as severe droughts. Combined with the possible increase of water demands, these changes exert a large pressure on the water systems. In order to confirm this assumption, a set of scenarios was proposed in this study to consider the combined impact of climate changes and the increase in water demand on the main multiple-use reservoirs of São Francisco River, Brazil. For this purpose, five CMIP6 climate models were used considering two greenhouse gas emissions scenarios: the SSP2-4.5 and SSP5-8.5. The naturalized and withdraw streamflows were estimated to the adopted reser-voirs considering all existing, new and projected demands. The conjunction of scenarios indicat-ed an increase in Potential Evapotranspiration, a possible significant reduction in water availa-bility, a growth in water demand mainly for irrigation and a substantial reduction in the perfor-mance of the evaluated reservoirs.

Combustion power plants emit carbon dioxide (CO₂), a major contributor to climate change. Direct emissions measurement is cost-prohibitive globally, while reporting varies in detail, recency, and granularity. To fill this gap and greatly increase the number of power plants worldwide with independent emissions monitoring, we developed and applied machine learning (ML) models using power plant plumes as proxy signals to estimate electric power generation and CO₂ emissions using Landsat 8, Sentinel-2, and PlanetScope imagery. Our ML models estimated power plant activity on each image snapshot, and then an aggregation model predicted plant utilization over a 30-day period. Lastly, emissions factors specific to region, fuel, and plant technology were used to convert estimated electricity generation to CO₂ emissions. Models were trained with reported hourly generation data in the US, Europe, and Australia and were validated with additional generation and emissions data from the US, Europe, Australia, Türkiye, and India. All results with sufficiently large sample sizes indicate that our models outperformed baseline approaches. In conducting external validation to compare modeled versus reported annual generation and emissions in the countries where available, we calculated the root mean square error overall for modeled countries with validation data as respectively 1.75 TWh (236 plants across 17 countries over 4 years) and 2.04 Mt CO₂ (207 plants across 17 countries over 4 years). Ultimately, we applied our ML method to plants that comprise 32% of global power plant CO₂ emissions averaged over 2015-2022. This dataset is the most comprehensive independent and free-of-cost global power plant point-source emissions monitoring system currently known to the authors and is publicly available at climatetrace.org to support global emissions reduction.

While the foundations of climate science and ethics are well established, fine-grained climate predictions, as well as policy-decisions, are beset with uncertainties. This chapter maps climate uncertainties and classifies them as to their ground, extent and location. A typology of uncertainty is presented, centered along the axes of scientific and moral uncertainty. This typology is illustrated with paradigmatic examples of uncertainty in climate science, climate ethics and climate economics. Subsequently, the chapter discusses the IPCC’s preferred way of representing uncertainties and evaluates its strengths and weaknesses from a risk management perspective. Three general strategies for decision-makers to cope with climate uncertainty are outlined, the usefulness of which largely depends on whether or not decision-makers find themselves in a context of deep uncertainty. The chapter concludes by offering two recommendations to ease the work of policymakers, faced with the various uncertainties engrained in climate discourse.

In this study, air temperatures were collected between 1985 and 2016 and compared to water temperatures in four locations in the distribution system of Pasadena Water & Power (PWP) that received imported surface water between 2001 and 2016 and from the purveyor of imported water. The concentration of chloramine residual and nitrite concentrations were collected between 2001 and 2016 these five locations. The results indicate that the median nighttime temperature of the period 2009 - 2016 was 1.6 ^oC warmer than the period of 1985 - 2000 and 0.5 ^oC warmer than the period 2001 - 2008. The median water temperature in the four distribution system samples increased by 0.8 ^oC to 1.4 ^oC depending on the location over the study period (p<0.001). The median chloramine concentration fell significantly (p<0.001) at three distribution system locations and the nitrite concentrations increased significantly at all four distribution system locations.

Ecosystem services (ES) are increasingly recognized as a means to adapt to the ongoing impact of climate change and associated impacts. However, these ES itself are facing adverse impact of climate change especially in developing countries where most of the people are dependent on these services for their livelihood. Very little is known about the relationship between the climate change and ES. Here we assess the impact of climate change on ecosystem services in ES rich landscape of Panchase Mountain Ecological Region of western Nepal. The study area was divided into three ecoregions from lowland through midland to the upland region. Focus group discussion, and key informant interview were used to elicit the required data for the study, which was further supported by transect walk, field observation and secondary source of information. Major impacts of climate change were observed are, reduced availability of water, reduced food production, forest ecosystem, shifting species composition in forest ecosystem, farmland abandonment, and their associated ecosystem services. We recommend to initiate the management actions to help ES adapt to climate change, and which in return could support the ecosystem itself and people dependent on the ES in adaptation to climate change by providing various goods and services.

This paper presents laboratory devices on which experimental measurements were carried out to prove the validity of the assumption about the reduction of vibrations transmitted to the conveyor belt structure generated by the impact forces of falling material grains in the places of transfer or on the hoppers of conveyor belts. In order to limit damage to the conveyor belts caused by the impact of the sharp edges of material grains, conveyor belts are supported by impact rollers or impact rubber rods in the places of transfers and on the hoppers of belt conveyors. The flattened ends of the impact rollers are inserted into the grooves of steel brackets installed on the supports of the fixed conveyor idlers of a conventional design. In this paper, a special modification of the fixed conveyor idler is presented, which consists of inserting plastic brackets into the structurally modified roller axle holders of the fixed conveyor idler. Measurements were carried out on the first laboratory device, which showed that the specially modified fixed conveyor idler resulted in a higher damping of up to 15% of the impact forces of the falling weight on the rubberized hoop of the impact roller shell compared to the conventional fixed conveyor idler design. Measurements carried out on the second laboratory device show that the effective vibration velocity values detected at the points where the impact roller axis fits into the fixed roller table holder, i.e. points B and D, are higher than when using plastic brackets, up to 6% for a 108 mm diameter roller, compared to steel impact roller brackets.

The World Health Organization estimates that every year air pollution kills seven million people worldwide. As it is expected that climate change will affect future air quality patterns, the full understanding of the links between air pollution and climate change, and how they affect human health, are challenges of future research. In this scope, a methodology to assess the air quality impacts on health was developed. The WRF-CAMx modelling framework was applied for the medium-term future climate (considering the SSP24.5 scenario) and for the recent past (considered as baseline). Following the WHO recommendations, mortality health indicators were used to estimate health impacts of long-term exposures. For that, the Aveiro Region, in Portugal, was considered as a case study. Future climate results indicate the occurrence of higher temperatures, and lower total precipitation. Despite that, improvements in the main pollutants’ concentrations, and consequently in the reduction of the related premature deaths are foreseen, mainly due to the reduction of pollutants emissions imposed by the European legislation for the upcoming years. The applied approach constitutes an added value in this research field, being crucial to anticipate the effects of climate change on air quality and evaluate their impacts on human health.

Nowadays, the growing concern about improving thermal comfort in textiles, asphalt pavements and buildings has stimulated research into phase change materials (PCMs). However, the incorporation of PCMs directly causes mechanical impacts. Therefore, this study herein reported focuses on designing coaxial phase change fibres using commercial cellulose acetate (CA) or recycled CA obtained from cotton fabrics (CAt) as a coating and polyethylene glycol (PEG) 2000 as a core. The phase change fibres (PCF) were produced by wet spinning, varying the parameters: (i) CA concentration, molecular weight and source (virgin versus recycled); and (ii) PEG concentration and ejection. For phase change recycled fibres (PCFt), PEG concentration and ejection were varied. The fibres were assessed for their optical, chemical, thermal and mechanical properties. Bright-field microscopy images and Scanning Electron Microscopy (SEM) micrographs proved the coaxial structure. Fourier Transform Infrared spectroscopy (FTIR) confirmed the presence of PEG in the core. Thermogravimetric Analysis (TGA) proved the ability of PCFs to tolerate high temperatures. Differential Scanning Calorimetry (DSC) attested to the presence of PEG2000 peaks, since their melting points were close to those of virgin PEG2000, with a slight change in the PCFs and PCFts caused by the protective sheath of CA and CAt.

The effects of climate change health impacts on urban health care systems and vulnerable populations are a researched topic with foundations on urban risk management, urban health methodologies, and sustainable urbanization. As climate change is a dynamic and transformative global issue the gap between adaptation and mitigation efforts and urban planning relies, among other things, in the limitations of previous studies of risk management structures tailored to the existing strategy to calculate the uncertainties and predict their influence on urban systems and subsystems. Moreover, as climate and health is a well-researched issue, it is also complex as the methodologies and strategic approach to minimize health risks of climate change is intrinsically contingent to risk methodologies and policy-making processes that are efficient with support of knowledge-based information. This paper emphasis the need for focusing on urban health risk management methods that serves to identify potential opportunities, and then manage and take action to prevent adverse health effects. It also emphasizes the probability of events and their consequences, which are measurable both qualitatively and quantitatively.

Future projections from climate models and recent studies shows impact of climate change on rainfall indices estimation. This study assesses the simulations of rainfall indices based on the Coupled Model Intercomparison Project CMIP5 and CMIP3 in the some of subbasin Hamedan Province West of Iran. The analysis of the rainfall indices are: simple rainfall intensity, very heavy rainfall days, maximum one-day rainfall and rainfall frequency has been carried out in this study to evaluating the impact of climate change on rainfall indices events. Relative change in three rainfall indices is investigated by GCMs under various greenhouse gas emission scenarious A1B and B1 and RCP8.5, RCP8.5 scenarios for the future periods 2020–2045 and 2045-2065. The final results show that each of rainfall indices differs in stations under the three GCMs model (GIAOM, MIHR, MPEH5) and emission scenarios A1B and B1, and RCP2.5, RCP8.5 scenarios. Relative change of daily intensity index varies from -9.93% - 25%, very heavy rainfall days 20.71% - 25.9% and yearly rainfall depth -15.71% - 13% can be observed at study area in 50y for future periods (2046–2065). Rainfall indices of sum wet days, nday >1mm and maximum one-day rainfall are projected to decrease under the senariuos B1,A1B and sum wet days, simple daily intensity and heavy Rainfall days>10 projected to decrease under the RCP2.6.

We revisit the notion of climate, along with its historical evolution, tracing the origin of the modern concerns about climate. The notion (and the scientific term) of climate has been established during the Greek antiquity in a geographical context and it acquired its statistical content (average weather) in modern times, after meteorological measurements had become common. Yet the modern definitions of climate are seriously affected by the wrong perception of the previous two centuries that climate should regularly be constant, unless an external agent acted. Therefore, we attempt to give a more rigorous definition of climate, consistent with the modern body of stochastics. We illustrate the definition by real-world data, which also exemplify the large climatic variability. Given this variability, the term “climate change” turns out to be scientifically unjustified. Specifically, it is a pleonasm as climate, like weather, has been ever changing. Indeed, a historical investigation reveals that the aim in using that term is not scientific but political. Within the political aims, water issues have been greatly promoted by projecting future catastrophes while reversing the true roles and causality directions. For this reason, we provide arguments that water is the main element that drives climate and not the opposite.

Statistical denoising techniques have been well studied, however many challenges still remain. This 9 article focuses on determining the least necessary number of significant samples in a statistical pop-10 ulation sampling, while minimizing the impact on the statistical detection of the fiber Bragg grating 11 (FBG) power spectra. This is achieved by using Bayesian decision making, small population statis-12 tics while excluding false detection. First, we investigate the impact of two-sided sliding window 13 technique applied near the cell under the test. The window size varies from 8 to 60 samples. The 14 considered maximum size of population sampling is connected with the number of wavelength 15 samples within the bandwidth of the symmetrical FBG power spectra peaks. Regarding the sym-16 metry of FBG power spectra peaks and the possible reduction of computational demands, we inves-17 tigate the impact of a one-sided sliding window. This enables to achieve less processing latency for 18 real time applications thus brings benefits to FBG-based fiber optical sensing. Both, two- and one-19 sided small population sampling techniques are then experimentally verified. Detection results are 20 analyzed in depth via calculating detection thresholds by using the small size window sliding along-21 side the waveband. From results achieved, it was found that the normality three-sigma rule does 22 not need to be followed while using the small population sampling. Experimental demonstrations 23 and analyses showed that the proposed concept of denoising and statistical threshold detection 24 based on the sliding window does not depend on the prior knowledge of probability distribution 25 functions of the FBG power spectra peaks level and background noise. We have also found that the 26 thresholds’ adaptability mainly depends on the statistical numerical characteristics of the above ad-27 ditive mixture of the FBG power spectra peaks signals and background noise.

The main purpose of this research is to analyze the perception of climate change impacts on human health in Bangladesh through data from nationality representative surveys conducted in some district of Bangladesh. In Bangladesh there have a few research has been conducted on public perceptions about the impact of climate change on human health. A structured questionnaire method was conducted, and data collected from 615 respondents. The findings of this study reveal that out of 615 respondents, 76.0% of the respondents replied positively while remaining 24.0%, almost one-fourth of total respondents, indicated that they have not heard the term climate change before. Knowledgeable in climate change, 92.5% of respondents agreed that climate change has an impact on human health while only 7.5% respondents disagreed with this statement. 90.5% of respondents argued that they are agreed with the opinion that climate change is a serious threat to human health.

The build-up of greenhouse gases (GHGs) is causing warmness in the Earth’s atmosphere, resulting in climate change. The transport sector is one of the active members of GHG emissions and it is imperative to use sustainable transport sources to control climate change. The measure aims to encourage citizens to stop using their private vehicles as their choice of transport and instead opt for joint sustainable mobility during traveling. In this study, a quantitative research method was used and data were collected from a sample of 410 respondents through questionnaire. On the other hand, study also took a simulation-based (N=10000) sample size of private vehicle data. The data were analyzed using structural equation modelling. The results revealed that sustainable transportation, climate change technologies and electric rail vehicles reduce climate change in the ecoregions of China. We conclude that sustainable transportation policies could be formulated and implemented to reduce climate change. In response to the research results, it is recommended that, since climate change is a multi-level governance issue and the outdated pyramidal transport industry models must be shifted to a sustainable transportation system model.

The Earth's fresh water resources predominantly is groundwater that is pumped out due to rapid urbanization and aggravated by climate change. Groundwater modeling is a crucial tool for understanding aquifer systems, employing 1D, 2D, and 3D numerical models with distinct applications. One-dimensional models focus on vertical dynamics, examining aquifer properties and simulating vertical contaminant transport. Two-dimensional models extend to regional scales, considering horizontal variations and assessing groundwater-surface water interactions, making them valuable for watershed-scale studies. Three-dimensional models provide a comprehensive representation of hydrogeological systems, capturing intricate flow patterns and aiding site-specific assessments. Comparative analysis reveals model strengths and limitations, emphasizing the importance of calibration for reliable results. Case studies showcase practical applications, such as 1D models in flooding analysis and 2D models for simulating debris flows. Three-dimensional modeling proves essential for understanding complex river-aquifer exchange fluxes. Future directions call for a global groundwater platform to address data variability and technical challenges. Despite substantial investments required, the anticipated returns in scientific advancements, societal benefits, and economic gains are expected to outweigh the initial costs. This review aims in highlighting the significance of advancing all three-dimension groundwater modeling for sustainable water resource management and environmental protection.

60\% of the global population is expected to be in cities by 2050. At the same time the climate is changing at an accelerated rate. In this short paper we discuss the conflation of these two phenomena and how it may change the cities of the future. We formulate this as an optimization problem that could help answer important questions such as where are they likely to be located? How are they to be powered? What would be their demand for mobility and how might this be met? How over or under ground might these cities be? This paper first looks at a historical paradigm of cities in the absence of the climate crisis and then proposes the new paradigm to look at cities of the future faced by the challenge of the climate change crisis.

Climate change poses a significant threat to global food security, necessitating a thorough examination across multiple dimensions. Establishing appropriate food security evaluation indicators that align with the evolving concept of food security is imperative. This study enhances food security evaluation by refining a multi-dimensional framework and analyzing the impact of climate change across various regions from 2002 to 2021. By constructing an Food Security Index system composed of a three-tier indicator system and employing the entropy method for weighting, we assess the impacts of climate change on food security using a climate-economic model. Regression analysis reveals a negative linear relationship between mean temperatures and food security (p&lt;0.01), while precipitation impacts food security non-linearly, particularly exhibiting inverse U-shaped patterns in major grain-producing and grain-consuming areas. Extreme high temperatures consistently reduce food security, whereas extreme precipitation displays a complex, statistically significant inverse U-shaped association. In regions where production and consumption are balanced, mean temperatures have a negative effect on food security, while precipitation exhibits a positive correlation, but excessive precipitation can have adverse effects. These findings highlight the intricate interplay between climate change, regional disparities, and food security in China, emphasizing the need to consider multi-dimensional factors and regional variations in addressing food security challenges. These insights are invaluable for policy-making and planning aimed at enhancing food security in China.

Background: Educating college students about the importance of good sleep habits could lead to the adoption of healthier sleep routines, including sufficient sleep duration and sleep quality. The current pre-post study examines the impact of completing a sleep education program called “Sleep 101” on (1) sleep hygiene practices, (2) sleep quality, and (3) weekday and weekend sleep behaviors among college students. “Sleep 101” is an interactive online course developed to educate college students about the importance of sleep by addressing basic sleep physiology, the impact of sleep on mood, academic and physical performance, the interactions between sleep and various substances, and common sleep disorders. Methods: 338 college students were recruited from a midsized public university in the US and invited to participate in “Sleep 101”, of which 25 participants completed the baseline and post-intervention questionnaire. The Sleep Hygiene Index (SHI) was utilized to evaluate sleep hygiene practices, while standard questions were employed to assess weekday and weekend sleep behaviors and overall sleep quality. Results: Those who completed the study demonstrated a significant increase in weekday sleep duration and were less likely to engage in negative prior bedtime activities. In addition, 40% of those who completed baseline and post-intervention assessments noticed improvements in their sleep quality. Conclusion: These findings suggest that a brief online program is beneficial in improving sleep behaviors among college and could be a scalable approach to addressing current sleep issues and preventing future sleep problems in college students.

This study was investigate the possible hydrological impact of climate change in stream flow of Arba River based on the downscaled precipitation and temperature data at West Harerghe Zone, Oromia regional state, Ethiopia. The case study, Arba River is one of the tributaries of Awash River Basin and the catchment of the river at Bordode flow gauging station covers around 72 square kilometers. Dynamically downscaled Regional Climate Model (RCM) HIRHAM derived by ICHEC-EC-EARTH global climate model under two scenarios RCP 4.5 and 8.5 was used in this study. The future time periods are divided into two time horizons with equal length of time as the base line period 2021–2050 and 2051–2080. For both projected periods relatively the minimum average monthly percentage change in precipitation will be happened from May to October under RCP4.5 except August and from March to October under RCP8.5. The variation under RCP4.5 is relatively high when compared to RCP8.5. The average monthly per-centage of flow increment may vary from (7.6 to 81.8) for 2021-2050 and (22.3 to 107.8) for 2051-2080 under RCP4.5. Under RCP8.5 scenario, from July to January except the December for 2021-2050, simulated flow is expected to increase while from February to June decreases for both time horizons. Generally, the change of simulated flow under both scenarios and both near and far time period is follows the pattern of precipitation. However, the flow change will have less variation than the precipitation change.

This literature review paper delves into the synergies between Climate-Smart Agriculture (CSA) and the effective management of food losses. With the escalating challenges posed by climate change and the pressing need to address food security concerns, the intersection of these two critical domains becomes paramount for sustainable agricultural practices. The review explores existing research on the implementation of climate-smart agricultural techniques and their impact on mitigating food losses throughout the agricultural value chain. The paper synthesizes findings related to climate-resilient crop varieties, precision farming, water-use efficiency, and sustainable soil management, highlighting their contributions to reducing post-harvest losses and enhancing overall food security. In addition to summarizing current knowledge, this paper identifies gaps in the literature and proposes avenues for future research. This review serves as a foundation for researchers, policymakers, and practitioners seeking to contribute to the advancement of climate-smart agriculture and the reduction of food losses, paving the way for a more sustainable and resilient global food system.

The power of movement in plants” by Charles Darwin details many examples of oscillatory growth exemplified more recently at the single cell level by pollen tube tip oscillations and associated ion fluxes, particularly Ca²⁺and H⁺. That implies an underlying growth oscillator based on the recent discovery that classical AGPs bind Ca²⁺ at the cell surface. The juxtaposition of AGPs with three other components embedded in the plasma membrane is evidence of a Ca²⁺ cycle that generates cytosolic Ca²⁺. It involves Ca²⁺ channels, auxin efflux “PIN” proteins and an auxin-activated proton pump that dissociates AGP-Ca²⁺ on demand. While the apparent simplicity of this system satisfies Occam’s Razor its hypothetical role as a global growth oscillator demands rigorous experimental appraisal. The wide ramifications extend from pollen tubes to stomatal guard cells. Stomata act as crucial components of a water hypercycle that contributes to homeostasis of a warming planet by regulating evaporative cooling, reflective cloud cover generated by vast forests and the high albedo of snow-capped mountains including polar ice-caps . The Gaia hypothesis of James Lovelock and Lynn Margulis is a brilliant metaphor.

Taeniid infections pose a significant threat to both animal and public health, as certain tapeworms within this group can also infect humans, potentially leading to severe health conditions. Therefore, this calls for preventive and control measures, such as regular deworming of dogs. While the efficacy of deworming has been established in developed countries, there is a paucity of reported deworming practices in rural areas of developing countries, such as Tanzania. This study aims to understand determinants for helminth treatment-seeking behavior towards control of cestodial taeniid infections in rural settings in northern Tanzania. A cross-sectional study was undertaken in agropastoral and pastoral areas of northern Tanzania. Comprehensive data from household surveys in selected sub-villages were collected to ascertain dog ownership, dog deworming practices, and the availability of dewormers. Analytical methods were employed to discern how various determinants influenced deworming practices among dog owners in these communities. Awareness of dewormers specifically formulated for dogs emerged as a pivotal factor affecting dog deworming practices. Dog owners who were informed about appropriate dog dewormers were nearly two times more likely to engage in deworming compared to those with limited knowledge (OR = 1.78, 95% CI 1.77 - 4.18, p < 0.001). A majority 32(51.6%, 95% CI: 38.7-64.3) acknowledged accessing dewormers for livestock within their wards. Praziquantel a potent dewormer on cestodes was least known to most dog owners 2(13%, 95% CI: 2.3-41.6). Praziquantel, a potent and common dewormer effective against cestodial taeniid infections, remains relatively unknown in rural areas of northern Tanzania. Therefore, more awareness on appropriate deworming agents against taeniids in dogs should be raised in rural dog-keeping communities.

The enterprise software sector is experiencing a significant transformation driven by rapid technological progress and evolving business needs. An effective release management process is crucial for the solution providers and customers, as they incorporate the principles of Industry 4.0 and 5.0. With the growing dependence on software solutions and services, ensuring their quality, reliability, and security is highly necessary. The release management process incorporates strategic planning, regression testing, accurate deployment, and continuous monitoring of software updates. However, the rapid advancements in artificial intelligence (AI) and machine learning (ML) create a unique challenge to the release management process. The existing research needs to address the necessary adaptations for Industry 5.0, mainly in product-based software organizations, to the release management process without a change management system. After working with several software solution stakeholders, based on the responses from SaaS project stakeholders, we have presented a revised software release readiness workflow by incorporating a change management module. The preliminary result based on data over a limited period of time indicates that the revised workflow has the potential to improve the management of slippage, particularly by more effectively addressing NFR-related issues, thereby resulting in more predictable and reliable release cycles.

Soil moisture can be an important indicator of climate change in humid and semi-arid areas. This indicator can more efficiently propose different public policies related to climate change than just using precipitation and temperature data. Given the above, the objective of this study is to evaluate changes in soil moisture in the state of Pernambuco during the period 1961-2021, using the System of Hydrological Response Units for Pernambuco. In this study, two river basins in the state of Pernambuco that represent the different climatic conditions of the state will be chosen. The results show that in the coastal region, there is a tendency towards more saturated soils, and in the semi-arid region, there is a tendency towards drier soils. With these results, it is possible to conclude that public policy decisions for the economy, environment, and society must consider this vital water balance variable.

Implementation of the German Climate change Strategy in the Federal State of Saxony-Anhalt is discussed in this paper. It shares the requirement and importance of sustainable development. An overview of strategy, The DAS Indicator System is provided with results of a portion of work being done for the ministry of agriculture by Deutscher Wetterdienst (DWD). Applicability of the indicator system is also shown by evaluation of results for specific indicators from 1961-2015.

COVID‑19 pandemic, also known as the coronavirus pandemic, has spread rapidly across the globe since the end of 2019 and brought impressive changes in our life and living through partial or full lockdown and reduced anthropogenic activities. Hence, it is imperative to investigate the impacts and consequences of COVID-19 on global eco-system and environment. The present study accordingly addresses the impacts and consequences of COVID-19 by ten environmental indicators; viz., global warming (or greenhouse gas emission), stratospheric ozone depletion, ozone formation (on human health), fine particulate matter formation, smog, ionizing radiation, human carcinogenic toxicity, human non-carcinogenic toxicity, water resource consumption, and effect on human health. The overall study has been performed in agreement with the standard principle and guidelines of life cycle assessment. Worldwide changes in consumption of fossil fuels, viz., petroleum, coal and natural gas, as a result of COVID-19 pandemic, has been the core theme of the study. Outcomes from the study show that COVID-19 has been a ‘blessing in disguise’ for the global environment with most of the above-mentioned indicators declining by approximately 20-30% in 2020 in comparison to the 2019 level.

Climate change impacts and adaptations is subject to ongoing issues that attract the attention of many researchers. Insight into the wind power potential in an area and its probable variation due to climate change impacts can provide useful information for energy policymakers and strategists for sustainable development and management of the energy. In this study, spatial variation of wind power density at the turbine hub-height and its variability under future climatic scenarios are taken under consideration. An ANFIS based post-processing technique was employed to match the power outputs of the regional climate model with those obtained from the reference data. The near-surface wind data obtained from a regional climate model are employed to investigate climate change impacts on the wind power resources in the Caspian Sea. Subsequent to converting near-surface wind speed to turbine hub-height speed and computation of wind power density, the results have been investigated to reveal mean annual power, seasonal, and monthly variability for a 20-year period in the present (1981-2000) and in the future (2081-2100). The results of this study revealed that climate change does not affect the wind climate over the study area, remarkably. However, a small decrease was projected for future simulation revealing a slightly decrease in mean annual wind power in the future compared to historical simulations. Moreover, the results demonstrated strong variation in wind power in terms of temporal and spatial distribution when winter and summer have the highest values of power. The findings of this study indicated that the middle and northern parts of the Caspian Sea are placed with the highest values of wind power. However, the results of the post-processing technique using adaptive neuro-fuzzy inference system (ANFIS) model showed that the real potential of the wind power in the area is lower than those of projected from the regional climate model.

In this paper, we first discuss the research status and application progress of the finite element method and the smoothed particle method. By analyzing the advantages of the smoothed particle method and the finite element method, a new coupling algorithm, namely FEM-SPH algorithm, is proposed. By the method of comparison, it shows that finite element method and SPH method in the simulation of large deformation problems each have advantages and disadvantages, the finite element method smoothed particle coupling algorithm is effective to achieve the performance of high computational efficiency and can naturally simulate large deformation problems across. In the process of calculation, the large deformation unit can be freely into an algorithm to facilitate the calculation accuracy and efficiency of three methods of numerical simulation. Through the study found, FEM-SPH algorithm not only overcome the defect of smooth particle tensile instability, but also overcomes the problem of low efficiency of finite element computation. To further test the FEM-SPH algorithm has advantages in the practical engineering, we have carried out the actual test to the example of the super high speed collision, concluded that, since the target of most of the computational domain is always finite element, smoothed particle focused only in contact with the projectile and target of local area, particle number is not much, the whole calculation process just ten minutes, computational efficiency has been greatly improved, at the same time in the simulation of large deformation, the advantage is very obvious .This provides a criterion for the actual project, depending on the specific material deformation mode and choose a more appropriate conversion algorithm.

This study assessed the impact of climate change on water availability and variability in two subbasins in the Upper Blue Nile Basin of Ethiopia. Downscaled future climate data from HadCM3 of A2 (medium-high) and B2 (medium-low) emission scenarios were compared to the observed climate data for a baseline period (1961 to 1990). The emission scenario representing the baseline period was used to predict future climate and as input to a hydrologic model to estimate the impact of future climate on the streamflow at three future time horizons 2020 - 2045, 2045 - 2070 and 2070 - 2100. Results suggest that medium-high emission scenario best represents the local rainfall and temperature pattern. With A2 scenario, daily maximum/minimum temperature will increase throughout the future time horizons. The minimum and maximum temperature will increase by 3.6^oC and 2.4^oC, respectively, towards the end of the 21st century. Consequently, potential evapotranspiration is expected to increase by 7.8%, though trends in annual rainfall do not show statistically meaningful trends between years. A notable seasonality was found in the rainfall pattern such that dry season rainfall amounts are likely to increase and wet season rainfall to decrease. The hydrological model indicated that the local hydrology of the study watersheds will be significantly influenced by climate change. Overall, at the end of the century, streamflow will increase in both rivers by up to 64% in dry seasons and decrease by 19% in wet seasons.

Photovoltaic (PV) technologies are developing rapidly as a result of their ability to reduce energy consumption from conventional sources as well as the growth of global energy expenditures. Nevertheless, several impact categories during the PV panel life cycle occur and some principal sectors are affected. These impact categories are assessed by applying life cycle analysis (LCA) techniques through various software programs, databases and methods. Such analyses of energy technologies are crucial, as they can reveal the occurrence of hazardous emissions at distinct life cycle stages. In the present paper, a PV panel impact assessment through life cycle analysis is carried out. More precisely, a combination of PV technologies—monocrystalline, multicrystalline, copper indium gallium selenide (CIGS), amorphous, and ribbon—is studied. The impact categories of ozone depletion, human toxicity, particulate matter, ionising radiation, photochemical ozone formation, acidification, eutrophication, and ecotoxicity, as well as the sectors of human health, ecosystem quality, and environment, are assessed using the openLCA software, the ecoinvent database, the eco-indicator 99, IMPACT 2002+, ReCiPe, and TRACI methods.

Electrochemical methods are used for the treatment of both municipal and industrial wastewater, either independently or in conjunction with biological methods. This conjunction is used for pretreatment or posttreatment of biologically treated wastewater. In our work, we focused on the combination of these processes, where pre-electrolysis was used to produce dissolved iron before the activation process. Electrolysis was also directly introduced into the activation using either iron or carbon electrodes. The surface of one iron electrode was 32.2 cm2, the applied voltage was 21 V, and the supplied current was 270 mA. The surface of one carbon electrode was 7.54 cm2, the supplied current was 82.5 mA, and the applied voltage was 21 V. From laboratory research on the treatment of synthetic municipal wastewater using a combination of electrolysis and activation processes, it resulted that the use of iron electrodes increased the efficiency of phosphorus removal compared to its precipitation with iron salts. The electrolysis had a positive effect on the sedimentation properties of sludge and the destruction of filamentous microorganisms. Electrolysis negatively affected the respiration rates of activated sludge and the efficiency of denitrification. However, it did not have a negative impact on the nitrification activity of the sludge.

Brain aging causes a wide variety of changes at molecular and cellular levels, leading to the decline of cognitive functions and increased vulnerability to neurodegenerative disorders. The research aimed at understanding the aging of the brain has made much progress in recent decades. Technological innovations such as single-cell RNA-sequencing (scRNA-seq), proteomic analyses, and spatial transcriptomic analyses have facilitated the research on the dynamic changes occurring within neurons, glia, and other cells along with their impacts on intercellular communication during aging. In this review, we introduce recent trends of how neurons and glia change during aging and discuss the impact of the brain microenvironment such as the blood-brain barrier (BBB).

Predators are dependent on the capture of prey to meet their energetic and nutritive requirements, imposing a regime of predation risk. Consequently, prey experience both consumptive and non-consumptive effects. Non-consumptive effects may manifest through altered growth and ontogenetic trajectories of prey species, a dynamic modulated by olfactory or other sensory cues from predators. Bactrocera dorsalis Hendel represents a great invasive threat to global horticulture. While earlier research has primarily centered on the consumptive interactions between B. dorsalis and its natural enemies, the potential consequences of non-consumptive interactions on the development of B. dorsalis have been overlooked. In this study, we investigated the impact of predation risk effects, induced by both visual exposure to the predatory mantis Hierodula patellifera Serville and its associated odor, on the life history traits of B. dorsalis. Female B. dorsalis demonstrated reduced development time in the presence of isolated H. patellifera or H. patellifera odor, yet evidenced significantly increased fecundity. Conversely, males displayed no significant change in development time. Additionally, neither female nor male body weight at death was significantly influenced by predation risk from isolated predators or predator odor. These results indicated adaptive developmental strategies of B. dorsalis when faced with risks from predators and highlight the important role of predator odor, which has the potential to lay the groundwork for improving biological control efforts and innovative approaches within pest control.

Students are often portrayed as future leaders. Their participation in climate change mitigation would improve when they access climate information and gain a high level of climate change awareness. This study was initiated to assess the Congolese students’ awareness of climate change by focusing on their sources of information on climate change, knowledge about the causes and impacts of climate change and activities that can raise awareness on climate change. Using a convenience sampling technique, we collected data through individual interviews conducted among 1,278 students from 13 universities across the Democratic Republic of Congo (DRC). The interview results showed that all students irrespective of their disciplines were concerned about climate change, a phenomenon strongly driven by human activities, such as deforestation (78%), urbanisation (30%), agriculture activities (30%) and transport services (26%). The students’ perceptions of climate change impacts included increase in temperature (82%), decrease in the number of rainy days (66%), proliferation of pests (60%) and increase in the number of malaria patients (39%). The primary sources of information that significantly affected students’ awareness of climate change included environment-related university courses and television broadcasts. The awareness-raising and mitigation actions related to climate change recommended by the students included educating people about good waste management (56%), planting trees (65%) and using the taxes paid by mining companies for the restoration of degraded ecosystems. The students believed that in DRC, all layers of the society (educational institutions, civil society organisations, community members and businesses) are important in building resilience to climate change. This study can guide teachers to focus their educational efforts on shaping pro-environmental behaviour in students.

Both academic literature and global organizations have emphasized the need for responsible water consumption, as stated in the Sustainable Development Goal 12. However, individuals’ water-saving behaviors in their current state are not enough. This situation entails a resistance to change (RC) in consumer habits and a lack of perceived risk of scarcity. The novelty of this study lies in examining the influence of RC (through its emotional, cognitive, and confidence components) and perceived risk on water-saving intention. Interviews (n = 384) were conducted in the southeast Mediterranean area of Spain by interviewers using a paper-and-pencil questionnaire. The results of the structural equation modeling show that the perceived risk and the components of cognitive rigidity and negative emotions exert a direct influence on water-saving habits and an indirect influence on water-saving intention. None of the components of RC directly influence intention, and a lack of confidence in the outcomes of water saving does not influence water-saving habits or water-saving intention. In addition to the results obtained, the novelty of the work lies in the idea that in order to influence the perception of the risk of water scarcity through awareness campaigns, it is better to use an emotional message rather than showing facts or information, because this does not drive water saving behavior.