In this study, we synthesized cobalt-doped molybdenum supported on silica (Co/MS) nanocomposites with varying concentrations of cobalt (1, 5, 10, 15, and 20 wt%) using the sol-gel method. We investigated their physico-chemical properties, photocatalytic activity, and antimicrobial efficacy. The synthesized nanocomposites were characterized using a range of techniques, including X-ray powder diffraction (XRD) to determine crystal structure, UV-vis spectroscopy for optical properties, Fourier transform infrared spectroscopy (FT-IR) for functional group analysis, and scanning electron microscopy coupled with energy-dispersive X-ray microanalysis (SEM-EDX) for morphological and elemental composition analysis. The photocatalytic performance of these catalysts was assessed by their ability to degrade organic dyes, specifically methyl orange and methylene blue, under visible light irradiation. Our results demonstrated that the photocatalytic efficiency increased with higher cobalt content, with the 20 wt% Co/MS nanocomposite showing the highest degradation rates. Additionally, we evaluated the antibacterial activity of the nanocomposites against a range of microorganisms, including Gram-positive and Gram-negative bacteria, as well as fungal species. The 20 wt% Co/MS nanocomposite exhibited superior antimicrobial activity compared to the other samples, indicating its potential for applications in environmental remediation and antimicrobial treatments.

The most significant air pollutant is particulate matter of less than 10 microns (PM10), followed by ozone (O3) during the monitoring period from 2006 to 2022 in Chiangmai. The association between ambient pollutants and climate conditions in Chiangmai was assessed using regression analysis and analysis of variance (ANOVA). The ANOVA analysis indicated that the average temperature was associated significantly with the nitrogen dioxide (NO2) concentration in the ambient, but the average rainfall volume was associated significantly with most pollutants except only sulfur dioxide (SO2). From the prediction models, the rise in average temperature affected to increase in the concentrations of PM10 and O3. Interestingly, the increase in rainfall will be advantageous to compromise the severity of all pollutants. Meanwhile, on hotter days should be careful of the rise of PM10 and O3 concentrations. Therefore, the vital meteorological variables associated with air pollution are very useful for forecasting the harmful and severity level of each air pollutant.

The mountainous region of the Hunza River watershed basin, especially along the Karakorum highway, and also known as a third pole for the high accumulation of glaciers, which leads to huge devastating landslides occurring every year. Landslide susceptibility mapping was carried out using two deep machine learning techniques (DeeplabV3+ & universal network U-Net) and two statistical models (Intuitionistic Fuzzy divergence IF-D & Frequency ratio FR). The landslide susceptibility mapping is conducted using landslide inventory data and twelve conditional factors. The landslide susceptibility maps obtained from the two statistical models were compared with those generated by two deep machine learning models based on prediction accuracy measures, such as the Area Under the Curve (AUC) and Seed Cell Area Index (SCAI). The Success Rate Curve (SRC) was obtained using the training dataset, and the AUC values for the four models were as follows: 76.9% for IF-D, 76.9% for FR, 80.4% for DeeplabV3+, and 76.3% for U-Net. In terms of the Prediction Rate Curve (PRC) obtained from the validation dataset, the AUC values were found to be 80.8% for IF-D, 80.8% for FR, 81% for DeeplabV3+, and 77.8% for U-Net. To assess the classification ability of the models, the Seed Cell Area Index (SCAI) test was conducted. The results indicated that the SCAI (D-value) was 7.3 for U-Net, 10 for DeeplabV3+, 7.6 for IF-D, and 9.1 for FR. Overall, the findings revealed that DeeplabV3+ exhibited the highest prediction accuracy and classification ability, making it the most suitable choice for landslide susceptibility mapping in the relevant study area.

Invasive weed plants are unwanted and hazardous waste biomass; and have extraordinary potential to serve as raw materials for biochar production. To evaluate the potentiality of invasive weed for bioenergy production in the form of biochar, Crotolaria burhia was investigated. The response surface modeling and optimization of the biochar parameters were conducted using the experimental design expert 13.0. The optimum value of the desirability function was obtained at a pyrolysis temperature of 450°C and a particle size of 50-100 mm. The model represents a p-value less than 0.0500 and a high F value, which denotes its reliable and accurate prediction of experimental data. A strong correlation was observed between actual and predicted values for biochar composites fixed carbon, carbon, surface area, pore size, and pore volume. In the present study, C. burhia biochar production was carried out by slow pyrolysis at 450°C under vacuum conditions. Biochar was found to be alkaline, with a 33.23% yield. Proximate analysis of C. burhia revealed 3.35% moisture content, 8.48% volatile matter, 81.24% fixed carbon and 6.94% ash content. The elemental analysis shows major concentrations of carbon, hydrogen, and oxygen as 57.77%, 6.123%, and 27.60%, respectively. Low H/C and O/C molar ratios were quantified as 0.10% and 0.47%, respectively. It possesses a honeycomb structure having mesoporous surface porosity with a surface area of 155.19m²/g and the presence of a remarkable concentration of mineral elements calcium and potassium. Biochar rich in hydroxyl, carboxylic, and alkene functional groups enhances its applicability areas. These findings make C. burhia a potential feedstock for the production of good-quality biochar.

Urban xenobiotics are a vital contamination phenomenon of urban plants in the overall country. They are a result of human activity due to growing urbanization and population growth. There are extensive sources of both natural (soil or rock erosion, fires, biodegradation, and volcanic eruptions) and anthropogenic (soil pollution, air, and herbicides). Currently, the demand for pharmaceuticals, compared to the growing population, has placed a risk on the urban plant. Additionally, the production of illegal drugs has caused the release of dangerous carcinogens into fungal activities, which will have an impact on plant health, microbial structure, and fungal interaction. Because of the harsh environment, higher temperatures, heavy metals, and higher N deposition, most urban trees suffer from stress conditions, and mycorrhiza is negatively impacted by plant conditions. Some mycorrhiza fungi are unable to sporulate and hyphal at higher xenobiotic concentrations in urban areas. This chapter takes a look at the sources and compounds of xenobiotics and their harmful impact on mycorrhiza; and its association with the urban plants.

Soil stabilization is a very important method of science and engineering for improving the properties of soil. This paper aims to stabilize expansive black cotton soil through a biological approach involving plant extracts, plant waste materials, and microorganisms. While chemical methods exist, i.e., lime stabilization, geotextiles, etc., they are not economically feasible for large-scale applications. The primary issue with black cotton soil is due to the presence of montmorillonite clay mineral, which makes it unsuitable for the construction of roads and airfields. The cation exchange capacity (CEC) can be defined as the ability of soil to absorb and exchange positively charged ions; thus, if free positively charged ions are not available, the soil will not exchange them with others. The CEC of the soil is diminished, and ultimately, the soil is stabilized to some extent. This paper explores the preparation of plant extract, which contains a high number of anions, and directly inoculates it with soil, which nullifies the positive charge of the soil and diminishes the CEC. The use of cellulose and lignin-degrading microorganisms as an energy source and other minerals that are needed for their growth will be utilized from the soil to reduce CEC, i.e., Mg required for DNA replication and Ca required for their growth and maintenance. Another approach to diminishing the CEC is to use the microorganisms that produce EPS, which require Ca and Mg as adhesions for the formation of biofilm, i.e., Pseudomonas aeruginosa, Bacillus subtilis, and Escherichia coli. The use of microorganisms that have specific enzymes is also used in the diminishing soil CEC, i.e., by using ureolytic enzyme-producing bacteria like Sporosarcina pasteurii, Bacillus paramycoides, Citrobacter sedlakii, and Enterobacter bugadensis.

In South Asian and Sub-Saharan African nations, climate change offers numerous hurdles to growth and development. These regions are susceptible to climate change due to their vast population reliance on agriculture, high demand for natural resources, and comparatively limited strategies for coping. Reduced food grain yields, crop losses, feed scarcity, lack of potable water for livestock during the summer, forceful animal migrations, and severe losses in the poultry and fishery industries have all been documented, posing a threat to the lives of the rural poor. As global food security and agricultural productivity become increasingly vulnerable, the focus has shifted towards adopting climate-smart agricultural practices and techniques. The present study discussed the need to identify and prioritize regionally evolving climate-smart farming practices and the enabling environment required for CSA uptake. The popular CSA practices in South Asia and Sub-Saharan Africa are crop rotation, cultivation of drought/flood-tolerant crops, legume intercropping, changing planting dates, rainwater harvesting, agroforestry, micro-irrigation technologies, minimum tillage, and integrated crop-livestock farming. A solid institutional structure, policy environment, infrastructure, agricultural insurance, climate information services, and gender and social inclusion provide the required enabling environment to alleviate farmer issues, lower CSA adoption obstacles, and improve operational sustainability. Highlights of the study are: This study examines how climate-smart farming practices are evolving in South Asia and SubSaharan Africa. We used a systematic approach to categorize and characterize agricultural adaptation alternatives to climate change. Our specific goals are to gain knowledge of the CSA adoption-enabling environments and the climate-smart agriculture practices employed in South Asia and Sub-Saharan Africa

This paper provides an in-depth analysis of the process of cleaning up crude oil-contaminated soil by using a carefully selected combination of bacteria that are capable of hydrocarbon breakdown. We assessed this bioremediation approach’s efficacy by evaluating tomato plant growth and vigor as indications of soil recovery. According to our research, adding hydrocarbon-degrading bacteria significantly enhanced the crude oil’s ability to break down in contaminated soil. Over time, the amount of petroleum hydrocarbons in the soil decreased significantly as a result of the bacterial consortium’s effective hydrocarbon metabolism. It became out that this bioremediation method was both economically and environmentally viable. Furthermore, we noticed significant improvements in the general health and growth of tomato plants grown in the bioremediated soil. These plants showed signs of excellent soil quality restoration, including higher biomass, enhanced root development, and less stress symptoms. This work highlights the possibility of bacteria-mediated bioremediation as a workable and long-term solution to soil pollution caused by crude oil. Additionally, incorporating plant growth monitoring highlights the ecological benefits of bioremediation as a remediation approach for repairing contaminated ecosystems and provides a useful way to assess the efficacy of bioremediation operations. The findings showed a substantial decrease in petroleum hydrocarbons and enhanced tomato plant growth in treated soils, demonstrating effective ecosystem restoration. By using bioremediation to treat soil contamination caused by crude oil, this research supports the conservation and sustainable use of terrestrial ecosystems, which is in line with Sustainable Development Goal 15: Life on Land.

Land Use and Land Cover (LULC) classification is critical for monitoring and managing natural resources and urban development. This study focuses on LULC classification for change detection analysis of remotely sensed data using a machine learning-based Random Forest classifier. The research aims to provide a detailed analysis of LULC changes between 2010 and 2020. The Random Forest classifier is chosen for its robustness and high accuracy in handling complex datasets. The classifier achieved a classification accuracy of 86.56% for the 2010 data and 88.42% for the 2020 data, demonstrating an improvement in classification performance over the decade. The results indicate significant LULC changes, highlighting areas of urban expansion, deforestation, and agricultural transformation. These findings highlight the importance of continuous monitoring and provide valuable insights for policymakers and environmental managers. The study demonstrates the effectiveness of using advanced machine-learning techniques for accurate LULC classification and change detection in remotely sensed data.

Paraben(s), or p-hydroxybenzoate derivatives, have been extensively used as preservatives in catalogs of products for decades. The chemical(s) of the group are well known for their water solubility, chemical stability, and low production costs. Additionally, these synthetic organics can be used as supplements in cosmetics, packaged foods, pharmaceuticals, and many other products requiring prolonged shelf lives. However, recent reports of parabenmediated endocrine disruptions, allergic responses, cancer, loss of fertility, and respiratory disorders are alarming and are the signs of growing health and environmental hazards. The unregulated disposal of packaged products supplemented with parabens and unintended uses may increase the environmental burden in the time to come. Recent studies exploring the health hazards associated with the use or consumption of compounds have provided insight into the underlying mechanisms of action. The paraben(s) are assimilated through two routes: oral administration and skin permeation. The ability to detect compounds in different environmental habitats with robust and specific techniques is important due to the unintended public health burdens of these compounds. This review presents the recent findings on the health burden of the compounds, fallacies in detection, and chronological advancements in the detection of paraben(s). This review assesses the impact of the increasing use of parabens on different cohorts, health hazards, and the need to develop more robust and accurate tools for detecting parabens in different environments.