This study aimed to identify ocean- and land-based sources of nutrients to the coral reef communities surrounding the Southern Caribbean islands Aruba, Bonaire, and Curaçao (ABC islands). The composition of water masses around these islands were assessed to depths up to 300 m and three distinct overlying water masses were identified, separated by mixing zones. A fluctuating pycnocline separating surface from deeper (>∼50 m) water indicated the presence of internal waves. Nutrient profiles were typical of tropical waters with oligotrophic waters occurring above the pycnocline and a deep chlorophyll-a maximum (DCM) just below it (∼65 m). Concentrations of dissolved nutrients differed among islands. Inorganic nitrogen (DIN) and phosphate concentrations were respectively lowest around Bonaire and Curaçao. The spatial distribution of chlorophyll-a (indicative of phytoplankton biomass), rather than nutrient concentrations, suggested the presence of higher-than-average nutrient concentrations in islands with higher population densities and near urbanized/industrial areas.

A bottleneck in restoring self-sustaining beds of the European oyster (Ostrea edulis) is the successful development and settlement of larvae to bottom habitats. These processes are largely governed by temperature but a mechanistic understanding of larval performance across ecologically relevant temperatures is lacking. We reared larvae at low (20–21 °C) and high (20–24 °C) fluctuating temperatures and applied short-term exposures of larvae to temperatures between 16 and 33 °C to assess vital rates and thermal coping ranges. Larval thermal preference was between 25 and 30 °C for both rearing treatments which corresponded with optimum temperatures for oxygen consumption rates and locomotion. Larvae had 5.5-fold higher settling success, however, when reared at the high compared to the low fluctuating temperatures. Higher mean and periods of increased temperature, as projected in a future climate, may therefore enhance recruitment success of O. edulis in northern European habitats.

Groundwater, an invaluable resource crucial for irrigation and drinking purposes, significantly impacts human health and societal advancement. This study aims to evaluate the groundwater quality in the Mnasra region of the Gharb Plain, employing a comprehensive analysis of thirty samples collected from various locations, based on thirty-three physicochemical parameters. Utilizing tools like the Pollution Index of Groundwater (PIG), Nitrate Pollution Index (NPI), Water Quality Index (WQI), Irrigation Water Quality Index (IWQI), as well as Multivariate Statistical Approaches (MSA), and the Geographic Information System (GIS), this research identifies the sources of groundwater pollution. The results revealed Ca2+ dominance among cations and Cl− as the primary anion. The Piper and Gibbs diagrams illustrated the prevalent Ca2+-Cl− water type and the significance of water–rock interactions, respectively. The PIG values indicated that 86.66% of samples exhibited “Insignificant pollution”. NPI showed notable nitrate pollution (1.48 to 7.06), with 83.33% of samples rated “Good” for drinking based on the WQI. The IWQI revealed that 80% of samples were classified as “Excellent” and 16.66% as “Good”. Spatial analysis identified the eastern and southern sections as highly contaminated due to agricultural activities. These findings provide valuable insights for decision-makers to manage groundwater resources and promote sustainable water management in the Gharb region.

This study investigated the efficacy of oxidised iron-loaded activated carbon cloth (Fe-ACC) for selective recovery of phosphorous. The capacitive deionisation (CDI) technology was employed, for rapid removal of phosphate, with the aim of reducing the reliance on high alkalinity environment for the regeneration of Fe-ACC electrode. Multiple experimental parameters, including applied potential, pH, and co-existing ions, were studied. Additionally, the CDI system was tested on a real water matrix (Lake Ormstrup, Denmark) to elucidate the electrodes’ performance on selective recovery of phosphate. About 69 ± 10% of the adsorbed phosphate were released at pH 12 via pure chemical desorption, which was ~ 50% higher than that at pH 9. The CDI system successfully demonstrated the selective removal of phosphate from the lake water. It reduced the concentration of phosphate from 1.69 to 0.49 mg/L with a 71% removal efficiency, while the removal percentages of other anions, namely chloride, sulphate, bromide, nitrite, nitrate, and fluoride, were 10%, 7%, 1%, 1.5%, 4%, and 7%, respectively.

Global warming and its consequences have heightened the urgency of reducing emissions of carbon dioxide globally. The concern arises from countries’ relentless efforts to achieve economic development at the expense of the environment. In this context, the paper examines the Environmental Kuznets Curve (EKC) hypothesis at the world level using carbon emission as an indicator of environmental degradation. The EKC hypothesis postulates an inverted U-shaped curve between economic development and environmental degradation; degrading environmental quality at the initial stages of development and, after a threshold level, environmental degradation lowers. The study investigates the validity of the EKC hypothesis for carbon emission with an analysis of 158 countries in the world, with population, urbanization, forest cover, and tourist inflow as the control variables. The study is based on secondary data collected from the World Bank. A regression analysis is used for the study. To ensure environmental sustainability, it is important to identify the determinants of carbon emissions across countries with varying levels of economic development. The findings of the study support the hypothesized inverse U-shaped association between Gross Domestic Product per capita and carbon emission per capita at the world level. Out of the four control variables, urbanization and tourist inflow were found statistically significant. Urbanization was positively correlated with carbon emission per capita while forest area was negatively correlated. Carbon emission per capita initially increases with rising GDP per capita and declines after GDP per capita reaches a certain level. The estimated turning point of GDP per capita occurs at a high level and therefore, most of the countries are anticipated to emit carbon dioxide.

Cryptocurrency has seen an increased popularity with the introduction of Bitcoins. It has been adapted in several countries and has become an alternate solution to conventional currency. Despite its benefits, some controversies surround the manufacturing of bitcoins. While all the countries are moving to sustainability development and global warming control, Bitcoin production has raised several concerns about environmental pollution and sustainability. The increased carbon emissions and high electrical consumption have accompanied the popularity of cryptocurrency. Hence, there is an immediate need to reduce the carbon footprint and electricity consumption caused by human cryptocurrency for a sustainable future. This study presents the current scenario and trends of worldwide cryptocurrency growth and discusses the environmental impact of cryptocurrency mining. It explores crypto mining worldwide and provides a qualitative review. Further, this article highlights the need to take necessary measures to control cryptocurrency circulation.

Watershed Development is a very common phenomenon in the river basins in India due to its dynamic and continuously changing nature, which are interconnected via. Land use/land cover (LULC) change and water poverty scenario over time. In the present study, the samples were chosen from seven sampled villages for the Water Poverty Index (WPI) in the upper Tons River Basin. Among them, Ghunwara and Maihar Village exhibit the highest and lowest WPI, i.e., 98.1 and 62.91 out of 100, respectively. This indicates that villages with a high WPI face challenges in their water requirements, regardless of the seasonal river serving the basin area. Conversely, villages with a low WPI can satisfy their water needs solely from the basin. The present analysis of the Upper Tons River Basin suggests that Land Use and Land Cover (LULC) will undergo influences or adjustments at various stages, ultimately affecting agricultural land in the impact region. It also becomes evident that areas with limited land use and land cover (LULC) extensions exhibit lower Water Productivity Index (WPI), primarily due to their reliance on agricultural land. It is observed that alterations, reductions, or modifications in LULC lead to changes in multiple aspects of agricultural land, resulting in noticeable variations in various metrics. The present paper not only evaluates the land use in the Upper Tons River Basin spanning from 2001 to 2021 but also highlights the changing patterns that impact water resources and their utilization capacity. Furthermore, the study estimates the influence of reducing specific features on the distribution of WPI and other LULC parameters. The Upper Tons River Basin faces challenges such as unfavorable rainfall patterns and inadequate planning for irrigation at the fundamental and local levels. Additionally, its geographical location in a rainfed area negatively affects the WPI.

Natural ecosystems have been facing a major threat due to deforestation and forest fires for the past decade. These environmental challenges have led to significant biodiversity loss, disruption of natural habitats, and adverse effects on climate change. The integration of Artificial Intelligence (AI) and Optimization techniques has made a revolutionary impact in disaster management, offering new avenues for early detection and prevention strategies. Therefore, to prevent the outbreak of a forest fire, an efficient forest fire diagnosis and aversion system is needed. To address this problem, an IoT-based Artificial Intelligence (AI) technique for forest fire detection has been proposed. This system leverages the Internet of Things (IoT) to collect real-time data from various sensors deployed in forest areas, providing continuous monitoring and early warning capabilities. Several researchers have contributed different techniques to predict forest fires at various remote locations, highlighting the importance of innovative approaches in this field. The proposed work involves object detection, which is facilitated by EfficientDet, a state-of-the-art object detection model known for its accuracy and efficiency. EfficientDet enables the system to accurately identify potential fire outbreaks by analyzing visual data from the sensors. To facilitate efficient detection at the outbreak of forest fires, a bi-directional gated recurrent neural network (Bi-GRU-NN) is needed. This neural network architecture is capable of processing sequential data from multiple directions, enhancing the system’s ability to predict the spread and intensity of fires. Crow Search Optimization (CSO) and fractional calculus are used to create an optimal solution in the proposed crow search fractional calculus optimization (CSFCO) algorithm for deep learning. CSO is inspired by the intelligent foraging behavior of crows, and when combined with fractional calculus, it provides a robust optimization framework that improves the accuracy and efficiency of the AI model. Experimental analysis shows that the proposed technique outperformed the other existing traditional approaches with an accuracy of 99.32% and an error rate of 0.12%. These results demonstrate the effectiveness of the integrated AI and optimization techniques in enhancing forest fire detection and prevention. The high accuracy and low error rate underscore the potential of this system to be a valuable tool in mitigating the risks associated with forest fires, ultimately contributing to the preservation of natural ecosystems.

Climate change can impact farmers’ incomes as agricultural production still depends on the weather. Currently, the majority of the impoverished rely primarily on agriculture for their income. The connection between poverty and climate change has been extensively studied, but further research is needed in this area. This research was conducted to provide empirical evidence regarding the impact of climate change on poverty using time series data, which has never been done. This research wants to examine the impact of socio-economics (economic growth, agricultural sector growth, inequality, inflation) and climate change on poverty. This research uses time series data from 2007 to 2022. The Central Bureau of Statistics and Climate Change Performance Index (CCPI) reports are the sources of research data. The study results suggest that the government’s performance index in combating inflation, agricultural sector growth, and climate change has a positive impact on poverty. Poverty is negatively affected by the Gini index and economic growth. Government efforts to adaptively address climate change are necessary to prevent worsening impacts on poverty rates. To reduce the risk of crop failure, farmers must also practice practical agricultural management.