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.

The effective management of organic waste from plant residues and food materials is essential for sustainable environmental practices and energy generation, particularly through the production of biohydrogen. This process involves the anaerobic digestion of organic waste, which can yield biogas primarily composed of methane at neutral pH levels. In contrast, an acidic environment (pH 4-5) promotes the generation of biohydrogen, a renewable energy source that contributes to the reduction of greenhouse gas emissions. Biohydrogen offers numerous advantages, including high energy efficiency, renewability, and environmental safety, as its combustion results in the release of only heat and water vapor, thus avoiding harmful effects associated with conventional fossil fuels. This study investigates the influence of various inoculum types on hydrogen gas production from organic waste, focusing on optimizing conditions for biohydrogen yield. Additionally, it explores the potential of residues from biohydrogen production as biodegradable agents for improving water quality. The findings highlight the efficacy of enzyme extracts derived from biohydrogen production residues in reducing key water quality parameters, such as Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), and Total Suspended Solids (TSS). By integrating sustainability principles, this research advocates for the recycling of biohydrogen residues as eco-friendly alternatives to conventional chemical treatments, thus contributing to both energy generation and environmental remediation efforts.

Flood risk modeling is essential for effective disaster mitigation, particularly in flood-prone areas such as Hulu Sungai Utara Regency, Indonesia. This study leverages Google Earth Engine (GEE) to integrate multi-source satellite data and machine learning techniques for flood susceptibility mapping. Key geospatial variables, including the Normalized Difference Vegetation Index (NDVI), elevation, distance from rivers, and the Topographic Position Index (TPI), were analyzed using a weighted overlay method within GEE. A supervised classification approach was employed to enhance accuracy, and validation was performed using historical flood event data. The results indicate that 51.66% (47,875.86 ha) of the study area falls into the low-risk category, 42.90% (39,763.08 ha) is at moderate risk, and 5.44% (5,040.36 ha) is highly susceptible to flooding. This study highlights the advantages of GEE in large-scale flood risk assessments by enabling real-time processing, high computational efficiency, and seamless integration of geospatial datasets. The findings provide critical insights for local governments and disaster management agencies to develop proactive flood mitigation strategies.

The recovery of acetic acid from aqueous sodium acetate using traditional methods is costly and energy-intensive. The current study focuses on exploring a synergetic reactive extraction methodology to extract acetic acid from aqueous sodium acetate waste. Physical extraction experiments demonstrated that methyl isobutyl ketone (MIBK) and xylene are effective diluents among MIBK, xylene, octanol, and toluene. The extraction efficiency was further enhanced by adding Aliquat 336 as an extractant and MIBK and xylene diluents in independent runs. Parameters such as the initial acid concentration in the aqueous phase, Aliquat 336 concentration in the organic phase, and temperature of the reaction mixture were investigated to optimize the operating conditions. Under all conditions, MIBK yielded better results than the other solvents. For a high acid concentration in the aqueous phase (0.5 moL.L-1), a 60% extraction efficiency was observed in the physical extraction experiment. The addition of Aliquat 336 as an extractant (0.729 moL.L-1) to the mixture under identical experimental conditions resulted in a 73% extraction efficiency. Average extraction improved by 10% for 0.2-0.5 moL.L-1 of initial acid concentration using reactive extraction technology. Such a recovery from aqueous sodium acetate using reactive extraction has rarely been reported, and hence, it is presented in this paper.

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.

Natural coagulants are eco-friendly and biodegradable alternatives to conventional inorganic coagulants. However, despite their proven efficiency in several investigations, the widespread adoption of innate natural coagulants for treating water and wastewater remains relatively low. This could be due to several factors, such as regulatory barriers, limited research, lack of awareness, and the need for further optimization of their performance. Industrial effluents contain a range of organic and inorganic substances, biological materials, and toxic compounds that can pose risks to public health and the environment. Traditional inorganic coagulants have been effective in wastewater treatment, but they can alter water characteristics and complicate sludge disposal. Natural coagulants are a potential solution owing to their efficient coagulation properties, lack of adverse effects on water characteristics, eco-friendliness, and biodegradability. Although natural coagulants have demonstrated efficacy in research, they have yet to gain widespread acceptance in the water industry, possibly due to various barriers. Five natural coagulants were screened, and four were used for further studies. A conventional jar test apparatus was used to perform the coagulation experiments. The tested coagulants removed 97% of the turbidity and 30-40% removal of the total dissolved solids and color. The removal of other chemical impurities, along with the aforementioned impurities, was governed by pH. Higher pH values of pharmaceutical wastewater reduced the efficiency of the coagulants. Nevertheless, lower coagulant doses were effective. These findings suggest a sustainable approach for the pretreatment or treatment of industrial wastewater.

Over the past decade, there has been a significant surge in harmful waste emissions of greenhouse gases, namely carbon dioxide, methane, and fluorinated gases, in the atmosphere. Two major categories of activities can be broadly identified as contributing to this condition. The first is the proliferation of worldwide industrial activity, as accounted for by industrial plants across all major continents. Second, human activity contributes to carbon emissions produced as a result of wide-ranging everyday activities that involve the use of electricity, transportation, food consumption, and other consumer-mindset-driven activities. This study focuses on the second category to build a dual-stage framework that will assess, evaluate, and recommend suitable mitigation measures to regulate usage patterns. The dual-stage approach is a novelty based on sound engineering principles. Carbon emission data gathered by the system were analyzed to detect footprint generation patterns using mathematical models. After the analysis, machine learning models selected from rigorous performance metrics (MAE, RMSE) were leveraged to predict the carbon footprint in the first stage. The second stage employs a reinforcement learning framework that captures several aspects of emissions in a ‘state’ and is used to analyze predictions and generate recommendations considering user preferences. The ability to absorb user goals for emission data is a strength. This unique finer engineering of state representation exemplifies experimental data that show minimal variation in state goal values within 2000 steps. A web application was developed to visualize various aspects, such as usage patterns and predictions. The user interface provides interventional, specific, and personalized recommendations. These aspects are then utilized to provide insights at the aggregated level in the context of a group of individuals, which is yet another strength of this framework. The extensibility of the proposed methodology for carbon emission mitigation for higher aggregated levels is demonstrated by an exemplar ‘location statistic’ radar chart in the context of the vehicle and electrical appliances categories.

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.

Research scientists worldwide are continuously driving innovations toward achieving a safe and healthy environment across the entire ecosystem. An integral component of this pursuit, as captured in SDG-7, is ensuring access to affordable, reliable, sustainable, and modern energy for all. The discovery of the vastness of bioresources embedded in agricultural and forestry residues mirrors hope and presents an array of challenges. Over the decades, biomass densification has been implemented to upgrade and consolidate the energy value of loose biomass for industrial and domestic applications. This is projected to mitigate the overreliance on fossil fuels as energy sources. However, the combustion and energy performance of biomass have not sufficiently met the energy mix requirements for extensive renewable energy use. The performance of the compacted material is dependent on the type of binder used in the manufacturing process, among other factors. This study explored the details of the available binders and biomass compositions investigated in previous studies. The authors also reported their performance, primarily regarding energy value and combustible behavior. Limitations such as low yield and low energy content, among other performance-related issues in biomass briquettes, can be highly enhanced with the appropriate selection of biomass and compatible binders. Hence, various research attempts, approaches, and methodologies have been conducted to develop solid fuel, and the binder’s influence on the energy content, density, combustion behavior, and other physical attributes of fuel briquettes has been reported.

The Air Pollution Tolerance Index (APTI) and Anticipated Performance Index (API) of Azadirachta indica, Ficus benghalensis, and Ficus religiosa were compared to assess their tolerance to air pollution in different environmental setups. The study was conducted at six different locations with different environmental setups, including Urban, Industrial, Institutional and Agricultural. The parameters used for APTI were pH, relative water content, total chlorophyll content, and ascorbic acid content in the leaves, while API was calculated using APTI along with the socio-economic characteristics of the targeted species. Three species were selected, with nine replicates of each species from each setup (i.e., 6×9×3, which means a total of 162 samples) were analyzed for APTI during the winter season, when there is a lower mixing height that prevents the dispersion of pollutants and makes the environment highly polluted, and trees show high tolerance in a polluted environment. The APTI values of all the targeted species were higher in industrial setups than in the other environmental setups, i.e., 20.42 ± 1.65 for A. indica, 14.75 ± 0.53 for F. benghalensis, and 13.39 ± 1.11 for F. religiosa. The sample t-test showed a significant difference in the APTI of the industrial setup and other setups (p=0.0000). A. indica was found to be a tolerant species, and F. benghalensis and F. religiosa were intermediate-tolerant species based on APTI. F. benghalensis and F. religiosa fall under the excellent and A. indica falls under the very good category based on API. Based on these two indices, the best tree species were identified for plantation and the abatement of air pollution in industrial areas.