Various wastes can be utilized to produce activated carbon, one of the wastes that can be utilized is nutmeg shell (Myristica fragrans). Activated carbon from nutmeg shells (Myristica fragrans) was used in this study to reduce the content of Pb(II) and Cu(II) ions in liquid waste. This research utilized the adsorption method with the batch system to determine the optimum contact time, optimum pH, and adsorption capacity. The characterization of activated carbon was done by Scanning Electron Microscopy (SEM) and Surface Area Analyzers (SAA). The content of Pb(II) and Cu(II) ions in the filtrate after adsorption was analyzed using an atomic absorption spectrophotometer (AAS). The results of SEM analysis showed that the carbon surface was cleaner and had more open pores after the activation process than before activation. The carbon surface area is 19.6243 m2.g-1. From the results of AAS analysis, the optimum time and pH for Pb(II) and Cu(II) ions was 40 min at pH 5 and 70 min at pH 4. With the Freundlich isotherm method, the adsorption capacity of the adsorbent for Pb(II) ions was 9.6028 mg.g-1 and Cu(II) ions was 0.035 mg.g-1, and the adsorption effectiveness on liquid waste for Pb and Cu metals was 1.9454 mg.g-1 and 0.4251 mg.g-1, respectively. The results showed that activated carbon from the nutmeg shell (Myristica fragrans) was able to reduce the levels of Pb(II) and Cu(II) ions in liquid waste.

The agricultural sector is seriously impacted by climate change, leading to potential risks to food security. In terms of global food production, maize ranks third. As a result, crop production and food security depend critically on assessing the effects of climate change and developing measures to adapt maize. Regarding adaptability, changing planting dates and using different agrochemicals are more effective than other management. Crop models are part of a global decision support system to help farmers maximize yields despite unpredictable weather patterns. To mitigate yield loss and protect the ecosystem, it is essential to use efficient maize-sowing practices in the field. This experiment was carried out to identify the most favorable sowing dates that maximize yield while ensuring the crop’s productivity and the integrity of the surrounding ecosystem remain intact. The main aim of this experiment was to mitigate the different climatic conditions by exogenous application of salicylic acid (SA) and sodium nitroprusside (SNP) on pigments and sugar content in maize under different sowing dates. A field experiment was carried out in the School of Agriculture, Lovely Professional University, Punjab, India, during the spring season of 2022. The experiment dealt with various maize crops, PMH-10, sourced from the Punjab Agricultural University (PAU), Punjab. The experiment was conducted in an open-air environment. The experimental setup was laid out in a split-plot design. The results stated that foliar application of salicylic acid and sodium nitroprusside successfully influenced high-temperature tolerance and low temperature at the reproductive phase and initial vegetative stages with other growing climatic conditions of maize in early and late sowings when controlled by increasing the chlorophyll index, carotenoids content, and sugar content of maize.

This study aimed to investigate the underlying anthropogenic driving factors of forest landscape degradation in the Kilimanjaro World Heritage Sites (WHS), Tanzania using survey-based data. The essence is to support strategic policies for forest landscape protection and natural heritage sustainability. The research employed empirical data using mixed questionnaires of experts and residents to identify various indirect anthropogenic driving factors of forest degradation, analyze rural poverty and causal mechanisms as indirect anthropogenic drivers of forest degradation, and evaluate the level of awareness and community involvement in forest protection. ArcGIS was used to generate the Maps. About 140 sample sizes were utilized for this study. Using purposive and simple random techniques, about 46 and 100 mixed questionnaires were distributed to experts in forest guard and residents, respectively. Data were analyzed using quantitative and qualitative techniques. Findings showed that indirect factors of forest degradation include high tourism demand, poverty, culture and tradition of local communities, lack of forest protection and conservation education, and insufficient land availability. Also, findings showed that rural poverty as an indirect anthropogenic driving factor of forest degradation is attributed to unemployment in rural areas, inadequate land for agriculture, and insufficient productive forestry availability. Additionally, this study revealed that residents are aware that the forest is under the government’s protection, and most people in local communities are not involved in activities for forest protection. Therefore, the study suggests that the locals should be involved in the activities that promote forest protection for effective control and management. Alternative heating methods should also be explored to reduce much pressure on the available forest to improve the natural heritage sustainability of natural WHS found in Sub-Saharan Africa and other parts of the Global South.

In this study, the AquaCrop model was employed to analyze the impact of projected future climate changes on the water usage and biomass production of potato crops in Babylon, Iraq, under varying irrigation methods. The irrigation techniques evaluated included sprinkler irrigation, surface drip irrigation, and subsurface drip irrigation at depths of 10 cm and 20 cm. The study involved simulating and forecasting conditions for the year 2050, comparing them to current conditions. The model measured and predicted the evapotranspiration (ETa) and actual biomass of potato crops for 2050 using the RCP 8.5 scenarios, which outline different trajectories for greenhouse gas emissions. The AquaCrop model was calibrated and validated using statistical measures such as the R2, RMSE, CV, EF, and D, achieving a 99% accuracy level in its performance. The findings suggest that using drip irrigation systems and applying the AquaCrop model significantly mitigates the adverse effects of environmental stress on desert soils and enhances sustainable agricultural practices in arid regions.

Global warming is one of the primary causes contributing to melting glaciers and shrinking of glaciers moth. Because of the glacier retreat, more lakes increase the risk of flooding in people’s homes and lives. Several studies on the surging glaciers have been conducted by researchers using various techniques, as well as with the aid of multiple models like the Normalized Differential Water Index (NDWI). The Number of glacial lakes is increasing in the Himalayan region due to climate change (rise of the temperature). Some glacial lakes are potentially dangerous so monitoring is very necessary. It is necessary to evaluate such vulnerable lakes. Therefore, current work is carried out to identify such glacial lakes present in the Teesta River Basin (Eastern Himalaya). Spatiotemporal Landsat data for the last four decades at intervals of ten years from 1990 to 2020 has been considered which was cloud-free and spatial resolution of 30 meters. The dataset mentioned above was used for lake identification and delineation. The findings indicate the presence of lakes with respective areas of 275 (18.90 km2), 337 (24.92 km2), 295 (22.96 km2), and 419 (31.44 km2). It has also been observed that the growth rate is increasing with approximate water spread from 1990 to 2000 (+129%), 2000 to 2010 (+106%), and 2010 to 2020 (+136%). The present study aimed to identify such glacial lakes based on their water spreading area, which is an essential step followed in the study of GLOF (Glacial Lake Outburst Flood) as it will be helpful in the identification of hazardous lakes. In that study, we found that eleven glacial lakes are in the potentially dangerous category situated in the upper Teesta Basin due to the presence of glaciers, which gives a clear reason for the time-to-time assessment of such lakes. By the conducted study it has been observed that the number of glacial lakes has increased, due to which water spread has also increased in the area. It can also be demonstrated that GIS (Geographical Information System), along with remote sensing, is one of the best tools for assessing and monitoring such change detection and differentiation of hazardous glacial lakes in the cryosphere, along with the supporting data.

This study aimed to check how herbicide 2,4-dichlorophenoxyacetic acid (2,4-D) affects the production of EPS and its composition, growth, and biomass, as well as morphology in a cyanobacterial species isolated from a rice field in Meghalaya, India. Compared to the control cells, the growth of the organism measured in terms of chlorophyll concentration increased after being exposed to 10 and 20 ppm 2,4-D. However, cultures treated with 30 and 40 ppm experienced a decrease in their growth. Likewise, the biomass content of the organism experienced a minuscule increase in content upon exposure to 10 and 20 ppm 2,4-D but was compromised upon exposure to higher doses. When exposed to 10 ppm, the total EPS content, which includes the RPS and CPS content, showed a substantial increase. Maximum EPS production was seen at 20 ppm 2,4-D. However, exposure to 30 and 40 ppm 2,4-D, EPS production in the organism experienced a significant reduction, respectively. All components of EPS, such as uronic acid, neutral sugar, and proteins, individually showed an increase in 10 and 20 ppm 2, 4-D. A similar trend was seen in the organism’s bio-flocculating activity, which increased when exposed to 10 and 20 ppm, respectively. However, this activity in cells exposed to 30 and 40 ppm 2,4-D was severely reduced. Not only the content of EPS but the rate of EPS production was also enhanced in lower concentrations of 2,4-D. Although exposure to 30 ppm 2,4-D, the rate of EPS production was not significantly compromised, 40 ppm exposure adversely affected the rate of EPS production. Furthermore, visualization using scanning electron microscopy revealed the morphological changes induced by the herbicide 2,4-D.

Cadmium (Cd) is ubiquitous and an unessential trace element existing in the environment. Anthropogenic activities and applications of synthetic phosphate fertilizers greatly enhance the concentration of Cadmium in the environment, which proves to be carcinogenic. The long-term effects of heavy metals contamination on plants and animals have recently become a major public health concern. Thanks to the application of science and technology, new environmental initiatives can have a lower environmental impact significantly. The role of microbes is very well known and must be considered as potential pollutant removers. Microbial flora can remove heavy metals and oil from contaminated soil and water. In comparison to conventional techniques, bioremediation itself proved to be a more potent technique because the established mechanisms render it ineffective. Biotechnological advancements are inherently harmful to the environment because they have the potential to reduce metal pollution. Pollutants in the environment can be effectively removed using bioremediation. Both native and introduced species can thrive in a microorganism-friendly environment.

This study investigates the physical characteristics of red clay using the IDW approach and linear regression modeling in an area of 268.12 km2, focusing on Kirkuk, Bor, and Jambor structures. Through the analysis of 52 soil samples and the integration of laboratory data with IDW and regression results, several significant findings have emerged. The IDW method combined with linear regression proves to be a cost-effective and efficient approach for obtaining soil property data and generating accurate digital maps of red clay’s physical features. The Silt concentration exhibits a wide range, while the gravel content remains relatively low, indicating the predominance of silt in the soil composition. Analysis of Atterberg limits reveals the soil’s behavior and consistency in response to moisture, with the plasticity index generally falling within the low to medium range due to the considerable silt content in most soil samples. The linear regression model highlights positive correlations between the liquid limit, plastic limit, and plasticity index. Moderately positive relationships exist between the liquid limit and clay content, as well as a weak positive association between the liquid limit and specific gravity. Dry density, on the other hand, shows no significant correlation with other physical variables, suggesting its independence from the measured parameters. The plastic limit demonstrates a stronger relationship with the clay content compared to the liquid limit. Additionally, weak positive correlations are found between the liquid limit, plastic limit, and specific gravity and water content, indicating the influence of moisture on these parameters. Furthermore, gravel exhibits a moderate positive correlation with sand and silt concentrations, while a strong positive correlation is observed between sand and silt contents, underscoring their close association with the soil composition.

Diseases caused by bacteria, fungi, and viruses are a problem for many crops. Farmers have challenges when trying to evaluate their crops daily by manual inspection across all forms of agriculture. Also, it is difficult to assess the crops since they are affected by various environmental factors and predators. These challenges can be addressed by employing crop disease detection approaches using artificial intelligence-based machine learning and deep learning techniques. This paper provides a comprehensive survey of various techniques utilized for crop disease prediction based on machine learning and deep learning approaches. This literature review summarises the contributions of a wide range of research works to the field of crop disease prediction, highlighting their commonalities and differences, parameters, and performance indicators. Further, to evaluate, a case study has been presented on how the paradigm shift will lead us to the design of an efficient learning model for crop disease prediction. It also identifies the gaps in knowledge that are supposed to be addressed to forge a path forward in research. From the survey conducted, it is apparent that the deep learning technique shows high efficiency over the machine learning approaches, thereby preventing crop loss.

This article discusses the major challenges related to greenhouse gas (GHG) emissions in the electricity sector and their impact on global climate change. The electricity sector is responsible for about a quarter of total global GHG emissions. To address these challenges, Life Cycle Assessment (LCA) is used to measure the environmental impact of different energy sources and electricity generation and distribution processes. The circular economy is presented as a promising approach to reducing the carbon footprint of the electricity sector. By optimizing the use and value of materials throughout their life cycle, this approach contributes to waste minimization and resource efficiency. Morocco is committed to reducing its GHG emissions and has adopted policies and regulatory frameworks to combat climate change. This study aims to calculate the climate change impacts of electricity distribution phases by applying a life-cycle approach to the case of an electricity distribution company in Morocco. This assessment makes it possible to identify significant contributors from each area. In the context of this company, it is a question of demonstrating how the application of the principles of the circular economy concepts contributes to the reduction of greenhouse gas emissions, in particular, that of scope 3. This study may be useful for other similar companies.