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.

Campus 2 of the National Institute of Technology (ITN) Malang shows its commitment to utilizing solar energy by adopting a 500 kWp photovoltaic solar power plant (PV), making it the largest in Indonesia for a private university. This research aims to evaluate the economic feasibility of photovoltaic solar power plants (PV) at Campus 2 of the National Institute of Technology Malang. The implementation of renewable energy, particularly photovoltaic solar power, is gaining attention due to its contribution to reducing greenhouse gas emissions and economic growth. However, the development of renewable energy sources faces several challenges, including the limitations of economic feasibility studies in Indonesia. A mixed-methods research approach is used, combining qualitative and quantitative data. Qualitative data are obtained from interviews with PV management staff, while quantitative data include net present value (NPV) calculations and payback periods (PBP). The research findings indicate that the on-grid photovoltaic solar power plant at Campus 2 of the National Institute of Technology (ITN) Malang has a capacity of 500 kWp, with a peak load reaching 380 kVA. The total project cost is Rp. 4,084,498,826, with annual operational and maintenance costs of Rp. 81,595,607. The price of electricity from the on-grid photovoltaic solar power plant is Rp. 930 per kWh. An NPV value of Rp. 7,789,395,602 indicates future profitability, while a PBP of 8.55 years demonstrates feasibility in terms of return on investment. In conclusion, the on-grid photovoltaic solar power plant at Campus 2 of the National Institute of Technology Malang has good economic feasibility due to factors such as controlled costs, competitive prices, a positive NPV, and a short PBP. Regular evaluations are necessary to ensure efficient operation and maximum benefits.

The present study focuses on estimating the Trophic State Index (TSI) of Renuka Lake, the smallest Ramsar site in India, utilizing in-situ observed Secchi disk transparency (SDT) and satellite data. Site-specific algorithms were developed by establishing the relationship between the spectral band ratio of Landsat 8 OLI and LISS-III with that of in-situ measured SDT data. Notably, the exponential regression model outperformed other regression models (linear, logarithmic, polynomial, and power), achieving a better model output (R2=0.94). Additionally, water quality parameters, namely pH and dissolved oxygen (DO), were measured using the TROLL 9500 multi-parameter instrument. Various interpolation methods were applied to the in-situ data, with the exponential regression model yielding the most accurate results.This method was subsequently selected to generate two-dimensional water-quality images of Renuka Lake. The combined analysis of in-situ and satellite-derived trophic status indicates the eutrophic to hypereutrophic condition of the lake’s eastern and western parts. Satellite imagery spanning 2010-2019 consistently reveals a eutrophic state in the lake, with fluctuations in intensity over the period. The sustained eutrophic condition is attributed to escalating human-induced activities surrounding the lake, particularly in the western region.

This study was conducted in Hezhang County, Bijie City, Guizhou Province. The soil in the zinc smelting area has been contaminated with cadmium, lead, and zinc. Therefore, these elements are the focus of this research. Rice husk biochar was used as the passivation material. The Fourier infrared spectrum was utilized to study the biochar’s morphology, element content, mineral composition, structure, and surface functional groups. Moreover, the physical and chemical properties of the biochar were analyzed to explore its passivation effect. Biochar is beneficial in the cleaning of cadmium, lead, and zinc minerals and can be used for the passivation of heavy metals in contaminated soil. This study aims to understand the detailed mechanism behind this process and provide experimental data and ideas for pollution control. The results indicate that the biochar contains many functional groups, including -OH, C-H, C-O, C=O, C=C, and C-O-C. It also consists of a significant quantity of potassium salt, calcite, and quartz. Biochar has a noticeable pore structure, and as the pyrolysis temperature increases, the pore structure becomes more developed and thinner, with a smooth surface. The main minerals in the soil are quartz, mica, zeolite, illite, and chlorite. The aromatic degree of biochar increased with pyrolysis temperature. In contrast, the aromatic degree and polarity first increased and then decreased. The 0.2-0.45 mm biochar exhibited the best passivation effect on cadmium, lead, and zinc.

The inhibitory effect of Rosemary oil on the corrosion of aluminum alloy EN AW-2011 in 1M H2SO4 solution was studied by weight loss and electrochemical methods such as open circuit potential (OCP), linear sweep voltammetry (LSV) and linear polarization resistance (LPR). The inhibition efficiency increases with increasing the concentration and shows maximum inhibition efficiency (70.7 %) at optimum concentration (0.05 g.L-1). The linear polarization resistance measurements show that the presence of Rosemary oil in 1M H2SO4 solution influences polarization resistance increasing and corrosion current decreasing. The voltammetric curve shows that Rosemary oil reduces the anodic process. Open circuit potential results confirmed that organic compounds present in Rosemary oil can form a protective layer on aluminum surfaces. The inhibitive effect was probably caused by the adsorption of organic compounds such as 1,8-cineole, α-pinene, borneol, limonene, and myrcene on aluminum surfaces which are non-toxic and environmentally friendly. This study showed that the essential oil of Rosemary could be used as an environmentally friendly inhibitor of the corrosion of alloy EN AW-2011 in an acidic medium.

Naturally derived cyanotoxins, cylindrospermopsin (CYN), and microcystin-LR (MC-LR) have shown hepatotoxic and nephrotoxic effects in several studies. The present study aimed to determine the possible nephrotoxicity of MC-LR and CYN on mammalian kidneys using male Wistar rats as an animal model. Potential nephrotoxicity was evaluated at different doses of CYN (0.175 μg.kg-1, 0.140 μg.kg-1, 0.105 μg.kg-1) and MC-LR (0.105 μg.kg-1, 0.070 μg.kg-1, 0.035 μg.kg-1) was observed. Water samples from dug wells contaminated with CYN (0.161 μg.kg-1) and MC-LR (0.091 μg.kg-1) from the Padaviya area in Anuradhapura, Sri Lanka were used as environmental samples. The control groups were treated with distilled water. The exposure time of rats to the toxin was 90 days. Evaluation of urinary creatinine, serum creatinine, and Kidney Injury Molecule-1 (KIM-1) were estimated using standard protocols. A significant increase in serum creatinine levels was observed in all CYN and MC-LR treated groups (p<0.05) after 7 and 42 days of exposure, respectively, compared to control. It was found a decrease of urine creatinine when rats were treated with different concentrations of CYN and MC-LR (p<0.05) after 7 days compared to the control. The highest KIM-1 concentrations were recorded at 0.175 μg.kg-1 of CYN and 0.105 μg.kg-1 of MC-LR. The concentrations of KIM-1 in the control groups for CYN-treated and MC-LR-treated were not detected. Luminal protein, nuclear pyknosis, mild tubular epithelial swelling, vascular congestion, and interstitial inflammation in CYN and MC-LR treated groups were common. No predominant changes were observed in the control groups treated with CYN and MC-LR. The results of the present study confirm that the consumption of CYN and MC-LR-contaminated water may lead to kidney injury in Wistar rats.

Outdoor air pollution causes a lot of health problems for humans. Particulate Matter 2.5 (PM2.5), due to its small size, can enter the human respiratory system with ease and cause significant health effects on humans. This makes PM2.5 significant among the various air pollutants. Hence, it is important to measure the value of PM2.5 accurately for better management of air quality. Algorithms for deep learning and machine learning can be used to forecast air quality data. A model that minimizes the prediction error of the PM2.5 forecast is needed. In this paper, a PM2.5 concentration estimation model using Bi-LSTM (Bidirectional Long Short-Term Memory) with meteorological data as predictor variables is proposed. For a better estimation of PM2.5 values, the hyperparameters of the Bi-LSTM model used are tuned using the Osprey Optimization Algorithm (OOA), a recent meta-heuristic algorithm. The model that works with the optimal values of hyperparameters identified by OOA performed better than the other models when they are compared based on evaluation metrics like Mean-Squared Error and R2.

There is a pressing demand for the introduction of environmentally safe technologies for the industries that supply the basic needs of industrialized societies. Advanced Oxidation Processes may become one of the answers to these uprising pollution management problems in the near future. The present investigation aimed to reduce the refractory organics present in the biologically treated (Activated Sludge Process) tannery effluent using Photocatalysis. The optimum time, pH, dosage of H2O2, and mass of NPAC required for the effective treatment using photocatalysis were found to be 60 mins, 8, 0.2 mg.L-1, and 1g. 100 mL-1, respectively. Although the efficiency of homogeneous photocatalysis was found to be higher than that of heterogeneous photocatalysis, the biodegradability was higher in the latter, with a value of 0.26. The experimental results have proved that photocatalysis could be a promising technology to reduce the refractory organics present in the tannery effluent.

This paper aimed to investigate the Spatial and Temporal Variation of the air quality index (AQI) in the Amman and Zarqa Metropolitan Areas during the period 2016-2022 following the method adopted by the Environmental Protection Agency of the United States of America (EPA). Air quality data for PM10, PM2.5, O3, NO2, SO2, and CO recorded at five monitoring stations were downloaded from the official website of the Jordanian Ministry of Environment. Calculated AQI values were generally between the Good class (AQI <50) and the Moderate class (AQI 50-100) at all stations, the AQI calculations for PM10 demonstrated a noticeable increase during autumnal months, likely due to natural dust. PM2.5 demonstrated seasonal variation, with higher values in winter months where residents burn fossil fuel for heating. Stabel air in winter due to the cooled land surface, and the weak natural air mix and ventilation contribute to the deterioration of air quality. Calculated individual AQI for SO2 and NO2 reveals that all extent of the study area falls in the Good AQI class. Similarly, CO and ozone-based AQI values fluctuate within the “Good” class, with occasional episodes of compromised air quality at specific stations.

Aquatic ecosystems that are subject to urbanization and environmental changes, such as the Kapaleeswarar and Chitrakulam tanks, depend on evaluating water quality. Their complicated data present challenges for conventional approaches. The usefulness of the Mamdani fuzzy inference system in determining the water quality in these tanks is investigated in this work. It creates a comprehensive assessment based on subject-matter expertise by handling ambiguous descriptors with linguistic variables and fuzzy sets. The system’s procedures for implementation are described in detail, with an emphasis on how well they can manage interrelated variables. The study shows how well the system measures the water quality in tanks and suggests ways to improve it. Tank evaluation that incorporates the Mamdani system encourages comprehensive resource management and cultural preservation.