Chromium contamination in water bodies poses severe risks to both the environment and human health. This research introduces an innovative solution to this challenge by creating a vapor-activated carbosorbent from biodegradable household waste. The efficacy of this adsorbent in removing total chromium through batch methods from aqueous solutions was investigated. Surface analysis using scanning electron microscopy (SEM) exhibited a porous structure, while Fourier-transform infrared spectroscopy (FTIR) identified distinct functional groups on the surface. The point of zero charge (PZC), determined at 6.95, revealed the adsorbent’s surface chemistry. Impressively, the synthesized carbosorbent exhibited significant adsorption capacities of 23.08 mg.g-1 for Cr(III) and 24.84 mg.g-1 for Cr(VI) under optimal conditions. The Langmuir isotherm model illustrated a monolayer adsorption mechanism aligned with the pseudo-second-order kinetic model, confirming chemisorption. Thermodynamic analysis disclosed favorable and spontaneous chromium adsorption. Negative ΔG° values affirmed the spontaneity, while the exothermic nature of the process was signified by the positive ΔH° value, indicating heat release. Increased randomness at the solid-liquid interface, indicated by the positive ΔS° value, underscored the enhanced affinity between the adsorbent and adsorbate. This study exemplifies the potential of the vapor-activated carbosorbent as an efficient and sustainable remedy for chromium-contaminated water bodies.

This research investigates the potential of two Plant Growth-Promoting Bacteria (PGPB) strains, EP1-AS and EP1-BM, isolated from the halophyte Euphorbia prostrata, to enhance plant growth and provide abiotic stress resilience. The study addresses the urgent need for sustainable agricultural practices in the face of challenges like soil salinization and heavy metal contamination. The investigation comprehensively analyzes the heavy metal and salt tolerance of the PGPB strains, revealing their potential applications in promoting plant growth under adverse environmental conditions. The research further explores the impact of these PGPB strains on wheat plants subjected to varying concentrations of heavy metals and salts. Results indicate that both PGPB strains, especially EP1-BM, exhibit significant tolerance to heavy metals and salt stress. EP1-BM demonstrates remarkable resilience even under high concentrations of these stressors. The study extends its findings to in vitro testing on wheat plants, revealing the positive influence of PGPB strains on germination, shoot length, and root length in the presence of salt and heavy metals. This research underscores the significance of understanding plant-microbe interactions, particularly in the context of promoting sustainable agriculture in challenging environments. The identified resilience of PGPB strains, especially EP1-BM, suggests their potential application as bio-remediators and plant growth promoters in soils affected by salinity and heavy metal stress. The promising results observed will be followed-up field trials. They will highlight the translational potential of these PGPB strains, offering a novel avenue for developing biofertilizer formulations with a cautious approach to safety concerns. Overall, this study contributes valuable insights into harnessing the untapped potential of resilient plants and their associated microbial communities for sustainable agriculture. It addresses key global challenges outlined by the United Nations Sustainable Development Goals.

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.

Dual concerns involving the rise in airborne pollutant levels and bulging need to protect-preserve human health have propelled the search for innovative means for air quality monitoring to aid in evidence-based decision-making (pollution prevention-mitigation). In this regard, moss bags have gathered a great deal of attention as active biomonitors. In this reflective discourse, we systematically review the world literature to present a bird’s eye view of moss bag applications and advances while highlighting potential concerns. We begin with a brief note on mosses as biomonitors, highlighting the advantages of moss bags over the passive technique (native moss), other living organisms (lichens, vascular plants), and instrument-based measurements. A major strand of moss bag research involves urban ecosystem sustainability studies (e.g., street tunnels and canyons, parks), while others include event-specific monitoring and change detection (e.g., SARS-CoV-2 Lockdown), indoor-outdoor air quality assessment, and change detection in land use patterns. Recent advances include biomagnetic studies, radioisotopic investigations, and mobile applications. Efforts are currently underway to couple moss bag results with a suite of indicators [e.g., relative accumulation factor (RAF), contamination factor (CF), pollution load index (PLI), enrichment factor (EF)] and spatially map the results for holistic appraisal of environmental quality (hot spot detection). However, while moss bag innovations and applications continue to grow over time, we point to fundamental concerns/uncertainties (e.g., lack of concordance in operational procedures and parameterization, ideal species selection, moss vitality) that still need to be addressed by targeted case studies, before the moss results could be considered in regulatory interventions.

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.

Biofuels are the cheapest source of energy, and the continuous decline of traditional sources of energy with the increasing population leads to looking for alternatives to reduce the consumption of traditional sources of energy. Bioethanol production from lignocellulosic wastes and cellulosic wastes is not a new approach for fuel production but a cheap and accessible way for the production of fuel. Bacillus is one of the major species that can act as a source of diversified enzymes. In this study, it was emphasized on screening and isolation of a novel, characterization, and best catalytic action on both celluloses and proteins in the presence of different carbon and nitrogen sources. It was observed the effective catalytic breakdown of cellulose with the crude enzyme to glucose allowed fur for fermentation with Saccharomyces, ultimately leading to the generation of alcohol. The study aims to isolate the microbes that can produce cellulases and enzymes and could be used for biodegradation to produce ethanol in the reaction. The maximum enzyme activity was achieved at 3.112 UI with optimized pH and temperature, and the maximum conversion of sugars into alcohol was about 70% in the newspaper, cartons, colored paper, and disposable paper cups. An essential observation was the decolorization of the origami craft paper within 24 hours. The study was involved in enhancing the maximum Enzyme activity of cellulases from different cellulosic raw materials. Hence, it was achieved by JCB strain, optimization of pH, temperature, and acids for the biodegradation. The presence of peaks at 3200 and 2900 was a confirmation of ethanol bonds in the biodegradation reaction mixtures.

The increased use of metallic nanoparticles has led to concern for environmental contamination and disruption in water quality. Therefore, effective screening of metallic nanoparticles is important for detecting metallic nanoparticles in aquatic environments. Biosensors offer several advantages, including high sensitivity to pollutants, short response time, energy efficiency, and low waste generation. In this study, a whole-cell biosensor was developed using microalga Chlorella vulgaris as a recognition element, and its fluorescence response was used as a measuring parameter for detecting the presence of titanium dioxide (TiO2) and silver (Ag) nanoparticles in water. The responses of C. vulgaris at the lag, exponential, and stationary phases to different concentrations of TiO2 and Ag nanoparticles were studied. The results showed that in TiO2 and Ag nanoparticles exposures, the highest fluorescence change (50-150%) was observed at the lag phase, whereas the lowest fluorescence change (40-75%) was observed at the stationary phase. A significant fluorescence change was observed in 15 min. The immobilized C. vulgaris under TiO2 and Ag nanoparticles exposures showed 30-180% higher fluorescence change than the negative control, indicating the potential of C. vulgaris as a biosensor for rapid detection of TiO2 and Ag nanoparticles in water. The mathematical modeling of the responses of C. vulgaris to TiO2 and Ag nanoparticles at 15 min of exposure with high R2 indicated that this biosensor is sensitive to the concentration tested (0.010–10.000 mg.L-1). Taken together, these results reveal that, for the first time, it is possible to detect TiO2 and Ag nanoparticles in water within a very short time using a microalgae-based biosensor. Moreover, no genetic engineering requirement makes this biosensor simple, economical, and free from the restriction on genetically modified microorganisms for environmental applications.

The study presented aims to find the most appropriate climate dataset for the data-scarce Jalaur River Basin (JRB), Iloilo, Philippines, by evaluating the statistical performance of five rainfall datasets (APHRODITE, CPC NOAA, ERA5, SA-OBS, and PGF-V3) with resolutions of 0.25° and 0.5° having a time domain of 1981 to 2005. Bilinear interpolation implemented through Climate Data Operator (CDO) was used to extract and process grid climate datasets with Linear scaling as bias correction to minimize product simulation uncertainties. The datasets were compared to the lone meteorological station nearest to JRB investigated at monthly and annual timescales using six statistical metrics, namely, Pearson’s correlation coefficient (r), coefficient of determination (R2), modified index of agreement (d1), Kling-Gupta efficiency, Nash-Sutcliffe efficiency (NSE), and RMSE-observations standard deviation ratio (RSR). The results indicate a strong positive correlation with the observed data for both rainfall and temperature (r > 0.8; R2, d1 > 0.80). Although graphical observation shows an underestimation of rainfall, goodness-of-fit values indicate very good model performance (NSE, KGE > 0.75; RSR < 0.50). In terms of temperature, variable responses are observed with significant overestimation for maximum temperature and underestimation for minimum temperature. SA-OBS proved to be the best-performing dataset, followed by ERA5 and PGF-V3. These key findings supply useful information in deciding the most appropriate gridded climate dataset for hydrometeorological investigation in the JRB and could enhance the regional representation of global datasets.

With the rapid advancement of Remote Sensing Technology, monitoring the agricultural land has become a facile task. To surveil the growth of paddy crops and provide detailed information regarding monitoring soil, drought, crop type, crop growth, crop health, crop yield, irrigation, and fertilizers, different types of remote sensing satellites are used like Landsat 8, Sentinel 2, and MODIS satellite. The main aim of Landsat 8, Sentinel 2 and MODIS satellites is to monitor the land and vegetation area and to provide data regarding agricultural activities. Each of these satellites possesses a different spectral band, resolution, and revisit period. By using the remote sensing spectral indices, different types of vegetation indices are calculated. This survey paper provides comprehensive about Remote Sensing and the major parameters that influence for growth of paddy crops, like soil and water, and the future scope of agriculture and its demand in research is discussed.

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.