Two isolates of Azotobacter spp. were isolated from the agricultural soil habitat of Jejuri village, Pune, Maharashtra, and identified using morphological characterization and biochemical tests. Zinc nanoparticles (Zn NPS) were synthesized from Azotobacter spp. and characterized by UV-Vis spectroscopy, Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD) and FESEM. XRD analysis showed the nanocrystalline form of zinc oxide NPs, and the Scherrer equation determined a mean crystallite size of approximately 42 nm. The FESEM spectra revealed the spherical shape and agglomeration of the biosynthesized nanoparticles. Various functional groups were involved in the capping and stabilization of the zinc oxide NPs, as confirmed by FTIR analysis. A pot experiment was conducted to study the effect of nanofertilizer prepared from zinc nanoparticles on parameters such as plant height, shoot length, root length, and dry leaf weight of Coriandrum sativum (coriander plant). In addition, parameters such as photosynthetic pigments, hormones, sugars, polyphenols, and proteins were tested. Mean values of measurements recorded on the 9th, 10th, 11th, and 12th days, along with standard deviation (SD) and percentage gain relative to the control measurements, were computed and reported. On the 12th day, the increase in height of the plant, length of the shoot, length of the root, and dry weight of the leaves in percentage were 9.24, 9.84, 10.86, and 11.71, respectively. On the 9th, 10th, and 11th days, the growth pattern was the same. To strengthen these results, advanced statistical analysis, such as analysis of variance (ANOVA), was performed and found that the growth in height of the plant, length of the shoot, length of the root, and weight of dry leaves significantly increased with respect to increasing days as well as increasing concentrations of nano fertilizer. Furthermore, to test significant differences in the mean chlorophyll content of both types of chlorophyll ‘a’ and chlorophyll ‘b’, a t-test for equality of population means was applied, and the results are reported. The results revealed that the nanofertilizer enhanced plant growth and health.

Cellulase, a key enzyme in breaking down cellulose, has significant applications in biomass, biofuel production, and environmental pollution control. This research investigated the optimization of crude cellulase production from Fusarium oxysporum isolates using solidstate fermentation (SSF). For cellulase production optimization, the fungal isolates were cultured to obtain pure cultures and identified based on genus characteristics. Inoculum was prepared by harvesting spores from Sabouraud dextrose agar (SDA). Solid-state fermentation was conducted with agro-based waste materials, including crushed agroresidues: date cores, wild reed, peanut shells, sunflower scales, corn cobs, banana peel, and sawdust, as substrates. Various parameters, including solid substrates, carbon and nitrogen sources, moisture content, incubation temperature and periods, and inoculum size, were optimized for cellulase production. Enzyme activity was measured by carboxymethyl cellulase (CMCase) and filter paperase (FPase) assays. The results showed that ten isolates of Fusarium spp. were identified, with isolate F4 demonstrating superior cellulase production compared to the others. This isolate was identified as Fusarium oxysporum. F4 isolate exhibited the highest cellulase index (CI) and specific activities for CMCase (17.33 U.mg-1) and FPase (8.62 U.mg-1). The optimal SSF conditions included corn cobs as the substrate, 60% moisture, and ammonium sulfate as the nitrogen source, yielding specific activities of 22.93 U.mg-1 (CMCase) and 10.61 U.mg-1 (FPase). The optimal temperature for cellulase production was 25°C, with peak enzyme activity observed after 120 h of incubation. The study’s findings demonstrated the potential of F. oxysporum for efficient cellulase production, particularly from inexpensive agro-residues, highlighting its industrial and environmental significance.

Land Use and Land Cover (LULC) classification is essential for monitoring environmental changes, managing resources, and planning sustainable development. However, accurate classification remains challenging because of the diversity of landscapes and the computational demands of processing large datasets. Among various machine learning (ML) algorithms, such as Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Random Forest (RF), and Classification and Regression Trees (CART), RF and CART were chosen for this study because of their robustness, simplicity, and efficiency in handling complex LULC classification tasks. This study focuses on the Brahmani-Baitarani River Basin, a region known for its environmental significance and susceptibility to land-use changes. Using remote sensing data from Landsat 8, Landsat 9, and Sentinel-2 satellites, a comparative analysis of RF and CART was conducted to evaluate their LULC mapping performance. The datasets were processed and analyzed on the Google Earth Engine (GEE) platform using multi-temporal image data and advanced filtering techniques. The results revealed that RF consistently delivered higher classification accuracy than CART, making it a reliable choice for LULC studies in dynamic and heterogeneous landscapes. By integrating high-resolution satellite imagery with ML algorithms, this study provided detailed insights into the spatial distribution of land use across the Brahmani-Baitarani Basin. These findings have practical applications in urban planning, natural resource management, and environmental conservation, and offer valuable information for decision-makers and researchers working to address global environmental challenges.

Bacillus megaterium, a phosphorus-solubilizing bacterium, has been exploited as a biofertilizer to increase crop yield. Thiamethoxam and chlorantraniliprole are novel insecticides that are applied as granular and foliar formulations for insect pest control. The present study evaluated the potential of B. megaterium for the bioremediation of these novel pesticides in natural and amended soils. The survivability of B. megaterium was studied in a liquid half-strength nutrient broth supplemented with thiamethoxam or chlorantraniliprole (5-100 mg.L-1). In addition, soil microcosm studies were conducted (21 days) to explore the bio-stimulating effect on the degradability of B. megaterium in pesticide-treated soils (@ 10 mg.kg-1) using organic amendments, vermicompost and Vesicular Arbuscular Mycorrhiza (VAM). The impact of pesticides was evaluated by calculating the enzymatic activities of dehydrogenase, phosphatase, and β-D-glucosidase. The experimental results revealed that B. megaterium could survive in pesticide-supplemented conditions, with maximum optical densities of 0.734 AU and 0.965 AU at 100 mg.kg-1 for thiamethoxam and chlorantraniliprole, respectively. Furthermore, these B. megaterium cultures also exhibited colony-forming units when plated on nutrient agar supplemented with thiamethoxam (21 × 105) and chlorantraniliprole (43 × 105) at the end of 21 days, indicating their adaptability. The soil application of B. megaterium combined with vermicompost or VAM exhibited higher degradation efficiency for thiamethoxam (2.61 and 2.16 mg.kg-1) and chlorantraniliprole (2.58 and 3.92 mg.kg-1), resulting in rapid degradation. The observed half-life values in these combined treatments were 11-12 days (thiamethoxam) and 11 and 15 days (chlorantraniliprole), which were on par with each other and significantly differed (two-factor ANOVA, p<0.05) when compared to natural attenuation (29–35 days). The enzymatic activity was negatively impacted for all enzymes under study. However, vermicompost amendments can recover enzymatic activity over time. Thus, B. megaterium has the potential to bioremediate thiamethoxam and chlorantraniliprole, and the application of soil amendments can reduce the sublethal effects of these pesticides.

The Middle East and North Africa (MENA) region faces significant challenges in sustainable development, digital technological advancement, and environmental protection, especially as pollution sources continue to increase and diversify. As MENA countries aim for economic progress, it is essential to understand how digital technological advancements impact their environment. Information and Communication Technologies (ICT) are vital for improving productivity and achieving development goals; however, they can also lead to pollution and increased CO2 emissions owing to their energy consumption and creation of electronic waste. Therefore, this study examined the short- and long-term effects of ICT, economic growth, institutions, population, non-renewable energy, and financial development on CO2 emissions in MENA countries from 2003 to 2021. This study investigates how ICT interacts with institutions and populations and explores how these interactions affect CO2 emissions. We used the panel autoregressive distributed lag (ARDL-PMG) model because it tests both short- and long-term effects. Additionally, it is effective with small sample sizes, making it ideal for our study, which includes only 11 countries. Our findings show that CO2 emissions are significantly affected, in the short and long terms, by ICT, economic growth, population size, and non-renewable energy. In contrast, effective institutions contribute to reducing emissions. Based on these findings, we offer economic recommendations to support sustainable development in the MENA region, aiming to balance technological advancement with the urgent need to address climate change and reduce CO2 emissions. Policymakers should integrate ICT with renewable energy, invest in sustainable sources, and enhance green technology regulations. Strengthening education on sustainable ICT practices and implementing energy efficiency policies will help reduce CO2 emissions in the MENA region.

Monitoring and evaluating soil quality is a trend in precision farming and sustainable agricultural management. This study used multivariate analysis to evaluate soil quality in durian-growing areas in Ben Tre, Vietnam. Twelve representative composite soil samples were collected, and nine selected soil indices were determined, including pH, EC, TOC, Bulk density, available phosphorus, NH4 + , CEC, clay content, humus content, and waterholding capacity. The dataset was transformed into new variables using principal component analysis (PCA), deriving relative weights (Wi) and soil normalization scores (Si), which were subsequently used to determine the soil quality index (SQI). The results of the study identified the MDS set consisting of three principal components that explained 84.33% of the variance in the dataset. The three indicators (including % clay, EC, and available phosphorus) represented the principal components. The current SQI of the study area was mostly at the average level (accounting for 83.3% of the area). The results of the SQI calculation based on PCA can help save time, reduce laboratory work costs, and support precise and efficient agricultural management.

Shoreline changes on the coast of Padang Pariaman Regency are influenced by hydrooceanographic and anthropogenic factors. Each village has different dominant factors. This research aims to 1. Analyze the contributing factors to changes in the village-by-village coastline. 2. Analyzing changes in village-by-village land cover in coastal areas. 3. Analyzing existing coastal protection buildings in each village. 4. Analyze the sedimentation contributions from the Limau and Batang Anai Rivers. This research uses a panel data regression method to determine the factors that influence shoreline changes, the Digital Shoreline Analysis System (DSAS) to determine shoreline changes, SAS Planet image analysis to calculate coastal protection structures, and the Jaelani algorithm to analyze sediment concentration. The study revealed that vegetation loss in Katapiang, Pilubang, Ulakan, and Tapakis villages significantly threatens coastal stability. Ineffective coastal protection exacerbates abrasion in these regions. Additionally, climate change increases the risk of threats to coastal areas. Land use changes in the watershed transport sediment to the estuary, causing coastal accretion and increasing the land area around Gisik Shoal. Overall, the coastline at the mouth of the Limau Watershed experienced more dominant accretion than abrasion owing to the high sedimentation process that occurred in 2003-2018. Simultaneously, the dominant shoreline change that occurred in the Batang Anai watershed is (abrasion). Each village has a different dominant shoreline change factor. Therefore, the solution applies to each region. Stakeholders must understand this condition to manage coastal areas more effectively.

The present study focused on isolating arsenic (As)-resistant bacterium from acid mine tailings of Changki, Nagaland, and evaluating its bioremediation potential. Isolation was performed using an enrichment culture approach and further characterized using standard procedures. The obtained bacterial strain AS3 was found to be resistant to As3+ and As5+ ions up to 1562 μg.mL-1 and 125000 μg.mL-1, respectively. The 16S rRNA gene sequence of the strain was found to be identical to that of Lysinibacillus sp. The growth behavior of the strain in the presence of selected heavy metals (HMs) showed a prolonged lag phase, especially in As5+. Moreover, the strain was found to be resistant to several antibiotics. SEM and EDX analyses revealed the presence of HM ions on the outer surface of the AS3 strain. The available functional groups on the surface of the AS3 strain cells engaged in the metal-binding process were identified using FTIR, suggesting their active participation in adsorption. AAS showed that the strain had the potential to remove As3+ and As5+ ions with removal efficiencies of 99.94% and 99.49%, respectively. Based on the findings, the strain exhibits intriguing biotechnological potential for HM bioremediation.

Air pollution caused by particulate matter is a critical global concern. In Thailand, particulate matter levels frequently exceed the standard threshold, particularly in the northern region, where severe haze episodes are common. These levels were notably higher during the early and late parts of the year, particularly in the dry season. Selecting an appropriate face mask is crucial for respiratory protection. To ensure that the mask used provides adequate filtration efficiency, an accessible, cost-effective, and reliable method for performance assessment is essential. This quasi-experimental study developed testing equipment to evaluate the filtration efficiency of particulate matter under simulated breathing conditions, focusing on the performance of the materials used in face mask production. The primary objective of this study was to design and develop testing equipment for comparing the effectiveness of commercial face masks in filtering PM2.5 and PM10. The study also evaluated and compared the filtration efficiencies of three types of commercially available face masks-fabric masks, surgical masks, and KN95 masks-alongside a control scenario without a mask. Data were collected by analyzing the particulate matter across various size ranges. The calibration process employed a reference gravimetric method (NIOSH 0500) to ensure accuracy (±5% deviation) and sampling pump airflow rates of 1-2 L.min-1. The results revealed that the KN95 mask exhibited the highest filtration efficiency, with an average particle concentration of 0.489 mg.m- ³ (SD=0.067), followed by surgical masks (0.572 mg.m- ³, SD=0.127) and fabric masks (0.944 mg.m- ³, SD=0.167). Wearing a mask significantly reduced the particulate matter concentrations compared to not wearing a mask (p < 0.001). Addressing Thailand’s severe particulate pollution requires an accessible and cost-effective device for evaluating mask filtration efficiency. Although the equipment used in this study was not industrial-grade, it effectively simulated inhalation conditions. The developed equipment achieved laboratorygrade accuracy, making it suitable for rapid testing in low-resource settings. Its simplified design ensures compliance with the NIOSH Method 0500 standards

Elevational gradients exhibit diverse patterns in species distribution and soil characteristics, creating distinct ecological zones. This paper aims to synthesize current knowledge on the relationships among elevation, environmental factors, plant families, and life forms to uncover the mechanisms driving these patterns. Species richness varies with elevation, with some plants declining while others thrive due to eco-physiological properties and functional traits. The review established that these elevational patterns differ among plant families and life forms. Intermediate elevation transition zones have more diverse floras, temperate species thrive at lower elevations than their primary habitats, and tropical lowland species expand to higher elevations. Climatic conditions, area size, the mid-domain effect, and biophysical processes, particularly water-energy dynamics, are crucial for understanding the intricate relationships between climate and vegetation. Changes in temperature and precipitation along altitudinal gradients significantly impact the attributes of soil. This review emphasizes the importance of microorganisms in nutrient cycling and highlights the adaptability of psychrophilic bacteria and fungi to high-altitude environments. Understanding these complex interactions is essential for predicting the impacts of climate change to ensure the sustainable management of high-altitude ecosystems. Further research into species richness patterns, soil dynamics, and microbial roles is necessary for developing effective conservation strategies and sustainable land management practices.