The project aims to remove reactive blue dye from the effluent of textile industries by utilizing coal-associated soil as an adsorbent, as it possesses effective physical properties and distinguishing characteristics. In comparison to other separation techniques, the adsorption method is the most effective, cost-effective, and straightforward. A batch adsorption investigation was carried out to examine the various adsorption-influencing factors, including solution pH, adsorbent dosage, contact time, temperature, and dye concentration. Contact time of 30 min, an adsorbent dosage of 10g.100 mL-1, a solution pH of 7, a temperature of 30°C, and an initial dye concentration of 100 mg.L-1 were found to be optimal for dye adsorption. Using two distinct kinetic models, the evaluation of kinetic studies revealed that the pseudo-second-order provided the greatest fit, with a higher R2 value than the pseudo-first-order. The thermodynamic parameters Gibbs free energy (ΔG°), entropy (ΔS°), and enthalpy (ΔH°) indicated that the current adsorption system was exothermic and spontaneous. Further study of the adsorption isotherm revealed that the Langmuir isotherm model provided the best fit, with an R2 value of 0.977%.

This study evaluated the seasonal variability of water quality in the Tilacancha River, the water source that supplies Chachapoyas, and the rural communities of Levanto and San Isidro del Maino of Perú. Eighteen physical, chemical, and microbiological water parameters were evaluated at five sampling points in two seasons (rainy and dry). To determine water quality, the results obtained for the parameters evaluated were compared with the Maximum Permissible Limits (MPL) established in the Regulation on Water Quality for Human Consumption (DS Nº 031-2010-SA), approved by the Environmental Health Directorate of the Ministry of Health. In addition, a Pearson correlation was performed to estimate the correlation between the variables evaluated. The results showed that microbiological parameters exceeded the MPLs in both periods evaluated, such as the case of total coliforms (44 MPN.100 mL-1), fecal coliforms (25 MPN.100 mL-1), and E. coli (5.45 MPN.100 mL-1), these microbiological parameters reported a positive correlation with turbidity, temperature, total dissolved solids, and flow rate. In addition, aluminum (Al) and manganese (Mn) exceeded the MPL in the rainy (0.26 mg Al.L-1) and dry (1.41 mg.Mn-1.L-1) seasons, respectively. The results indicated that the water of the Tilacancha River is not suitable for human consumption. Therefore, it must be treated in drinking water treatment plants to be used as drinking water.

The Vamanapuram River Basin (VRB) is home to a diverse range of plant species, including 152 distinct species from 50 botanical families. Poaceae, Leguminosae, Araceae, and Aseraceae are the most abundant, with 13 species. Euphorbiaceae, Acanthaceae, Apocynaceae, and Rubiaceae also contribute to the biodiversity hotspots. The VRB’s vegetation profile is characterized by a dynamic interplay of plant forms and ecological niches, with 74 herbs, 30 shrubs, 12 grasses, 1 liana, and 35 towering trees. The Poaceae family thrives in this environment due to hydrological factors. The sampling sites P6 and P5 exhibit high relative frequency and density, with key species like Macaranga peltata, Ficus hispida, and Swietenia macrophylla. Diversity indices like the Shannon-Wiener diversity index reaffirm the VRB’s tropical forest character. Beta-diversity patterns reveal unique plant species distribution dynamics among different panchayaths, emphasizing their ecological complexities. The study emphasizes the demand for specialized management and conservation techniques in this environmentally active region.

Over time, predictive models tend to become more accurate but also more complex, thus achieving better predictive accuracy. When the data is improved by increasing its quantity and availability, the models are also better, which implies that the data must be processed to filter and adapt it for initial analysis and then modeling. This work aims to apply the Random Forest model to predict PM10 particles. For this purpose, data were obtained from environmental monitoring stations in Mexico City, which operates 29 stations of which 12 belong to the State of Mexico. The pollutants analyzed were CO carbon monoxide, NO nitrogen oxide, and PM10 particulate matter equal to or less than 10 μg.m-3, NOx nitrogen oxide, NO2 nitrogen dioxide, SO2 sulfur dioxide, O3 ozone, and PM2.5 particulate matter equal to or less than 2.5 μg.m-3. The result was that when calculating the certainty of our model, we have a value of 80.40% when calculating the deviation from the mean, using 15 reference variables.

Aging oil is a common pollutant in petrochemical enterprises due to its severe emulsification and flocculation, poor settling performance, low oil recovery rate, and high difficulty in treatment. This article adopts the method of mechanical, ultrasonic, and chemical coupling demulsification to treat aging oil, with the water content and oil recovery rate of the treated aging oil as the inspection indicators. The experiment shows that when the oil-water ratio is 1:4, the heating temperature is 50℃, the stirring speed is 180rpm, the ultrasonic frequency is 25kHz, the power is 40W, the ultrasonic time is 25min, and the pH is adjusted to 3-4. The additional amount of FeSO4 is 160mg/L, the additional amount of H2O2 is 0.11%, and the heating stirring reaction is 40min. When the dosage of cationic PAM with an ion degree of 50 is 35mg/L, the centrifugation speed is 3200rpm. The centrifugation time is 20 min, the crude oil recovery rate after aging oil treatment can reach over 94.6%, and the water content of the treated crude oil is less than 0.5%, meeting the standards for crude oil gathering and transportation in China. The oil content in the water generated after aging oil treatment is about 150 mg.L-1, the suspended solids content is 200 mg.L-1, the oil content in the residue is 6%, and the water content is 53%. By analyzing the appearance of aging oil before and after treatment, it was found that when using this process to treat aging oil, the original spatial cross-linking network structure of the aging oil was broken, allowing the water droplets wrapped in the oil to be released, thereby significantly reducing the water content in the recovered oil and improving the oil recovery rate.

The present study attempted to observe the perception and adaptation strategies of farmers in the context of climate change. It observes that the majority of the farmers are aware of climate change and understand that they are facing problems due to it. The major problems faced by the farmers are the long duration of dryness due to lack of rainfall, weed pressure, very high temperatures, and crop disease. However, farmers are not very aware of technological adaptation and have changed the cropping time due to changes in the time of monsoon. The study recommends that there is a need for intensive micro and macro policy initiatives in terms of modern green sustainable technology along with awareness and skill development of the farmers. The government should also focus more on policy initiatives for sustainable agricultural practices in line with sustainable development goals.

Assessing the dynamics and patterns of Land Use and Land Cover (LULC) and its transformation is an important practice of urban planners and environmentalists for a variety of applications, including land management, urban climate modeling, and sustainability of any urban region. Monitoring changes in LULC using geospatial techniques can help to identify areas at risk for indefensible land use, low-grade environment, and especially for sustainable urban planning. This study aims to analyze the changing pattern, dynamics, and alteration of LULC using Google Earth Engine (GEE) and Machine Learning Applications for the years 1991, 2001, 2011, and 2022 in the Varanasi City Development Region (VCDR). The LULC classification was divided into seven classes using random forest classification, and Landsat-5(TM) and 9(OLI-2) satellite data were used. Saga GIS has been utilized for the detection of LULC change during the 1991-2022 period. For validation of classification results, accuracy assessment was estimated using error matrices and through user, producer, and overall accuracy estimation. The Kappa statistics were applied for the reliability of the accuracy assessment result. As a result, the built-up area increased by 507.8 percent, and other classes like agricultural, barren, fallow land, and vegetation cover rapidly declined and altered into concrete areas over the period. Water bodies and river sand classes have been slightly converted into different classes. The finding explains that 114.8 km2 of fertile agricultural land, 14.81 km2 barren land, and 12.93 km2 of vegetation cover transformed into impervious surface, which is unsustainable and causes various problems like food scarcity, environmental degradation, and low quality of urban life. This study can be a useful guide for urban planners, academicians, and policymakers by providing a scientific background for sustainable urban planning and management of VCDR and other cities as well.

Lignin, being highly resistant, needs to be eliminated in the process of extraction of soluble reducing sugar and bioethanol production from lignocellulosic biomass. In the present work, pretreatment of sugarcane bagasse (SCB) was performed using NaOH of various concentrations (1-5%) to facilitate delignification. The hydrolysis efficiency of pretreated SCB was evaluated at different reaction times by the production of reducing sugar using the Cellic CTec2 enzyme. The maximum cellulose content of 57.6% and lignin removal of 62.04% were observed with 2% sodium hydroxide at 121°C autoclaved for 60 min. The hemicellulose content decreased with increasing NaOH concentration with the maximum decrease of 13.6% from native bagasse having 26.5% xylan content. The microstructure, morphology, and chemical composition of SCB were analyzed using Field Emission Scanning Electron Microscopy (FESEM), Fourier Transform InfraRed (FTIR), and XRD. The hydrolysis with 10 FPU.g-1 of enzyme at 48 h of reaction time shows a maximum yield of 12.34 g.L-1 corresponding to 55.53 ± 0.45% at 2% NaOH pretreated SCB. This study claims that lignin components exhibited the highest susceptibility to NaOH pretreatment, which directly affects enzymatic hydrolysis.

The coir industry in India’s southern coastal regions, especially in the state of Kerala, is becoming increasingly concerned about the environmental impact of the accumulation and incremental increase of coir pith each year. The objective of this study was to assess the effect of pretreatment on the anaerobic digestion of coir pith. The characterization study of coir pith shows high organic content, which can be anaerobically digested to produce biogas. But, the high lignin content (30.91%) makes the process slow. To overcome this, a biological pretreatment method was tried using two microbial cultures belonging to fungal genera known to be lignin decomposers, viz., Trichoderma and Pleurotus. By using Trichoderma, lignin content was reduced by 3.7%, and the maximum gas production was obtained in a shorter time (19 days) in comparison with the sample without any pretreatment (24 days). When Pleurotus was used for lignin degradation, the lignin content was reduced by 6.78%, and the maximum gas production was obtained in a much shorter time period (14 days) in comparison with the former two methods. The gas produced comprises 74 ppm of methane, which has fuel value. The sludge after digestion was tested, which indicated a marginal increase in NPK value and hence can be used as fertilizer. The results of the study appear to be quite promising in the transition towards green energy by providing scope for the process of biomethanation, with the conclusion that further research can transform coir pith into a good renewable energy resource.

For many years, especially in emerging nations like India, the environment has been threatened by the increased output of industrial wastes and heavy metal toxicity. The usage of inexpensive adsorbents has recently attracted a lot of attention in studies on the removal of heavy metals like nickel from industrial wastewater. The use of agro-based adsorbent is an alternative to conventionally used activated charcoal. In this research, adsorption experiments were carried out using agro-based adsorbent prepared from rice husk, wheat husk, and soybean husk to reduce nickel from industrial wastewater. The adsorption process is simple, economical, and effective is the most preferred method used for the removal of toxic metals like nickel from industrial wastewater. Adsorbents prepared from these husks can be effectively used for adsorption due to low cost & high availability. Characterization of agricultural material by various tests like XRF, proximate analysis, bulk density, and iodine number was conducted on agro-based adsorbents to know the co-relation between removal efficiency and adsorption capacity. The effect of turbidity and pH parameters on Ni removal efficiency is also studied. Results indicated that wheat husk adsorbent appeared to be the most effective for the adsorption of Ni from wastewater as compared to soybean husk and rice husk adsorbent. Wheat husk, soybean husk, and rice husk have removal efficiency in the range of 62.50 to 73.33. Composite absorbents CA-2 with the proportion of 50% wheat husk, 33% soybean husk, and 17% rice husk have 82.50% efficiency, and CA-3 has 80.83% efficiency in removing Nickel. Wheat husk adsorbent, CA-2, and CA-3 are more effectively and sustainably used for the treatment of industrial wastewater to remove heavy metals.