Globally, soil erosion is a significant problem contributing to nutrient loss, water quality degradation, and sand accumulation in water bodies. Currently, various climate factors are affecting the natural resources entire worldwide. Agricultural intensification, soil degradation, and some other human impacts all contribute to soil erosion, which is a significant issue. Management and conservation efforts in a watershed can benefit from a soil erosion study. Modeling can establish a scientific and accurate method to calculate sediment output and soil erosion below a variety of circumstances. The measured soil loss tolerance was compared to the risk of soil erosion (T value).In this study, GIS and remote sensing techniques have been integrated with the Revised Universal Soil Loss Equation (RUSLE) model to estimate soil loss in the Mayurakshi river basin of eastern India. To determine soil erosion-prone areas, rainfall, land use, and land cover maps, as well as a digital elevation model (DEM), were used as input. The annual soil loss in the basin area is estimated to be 4,629,714.8 tons. Accordingly, the study basin was categorized into five soil loss severity classes: very low (40.92%), low (49%), moderate (6.5%), high (2.4%) and very high (1.18%) risk classes. Soil erosion rates ranged from very slight to slight throughout the majority of the region. The section of the basin’s lower plain has been discovered to be least affected by soil loss. The results of study area can be helpful to conservation of soil management practices and watershed development program in the basin area.

This study concerned the assessment of changes in the coastline of Kavvayi beach and its nine islands in a tropical coastal wetland using remote sensing (RS) data and geographic information systems (GIS), as this sedimentary environment is still poorly characterized. The satellite data used were from LANDSAT images (30 m spatial resolution) (MSS—volume 5, TM, ETM, and ETM+ volume 6) for the period of the last 25 years (1990 to 2014). Band 5 in Landsat (MSS) and band 6 (ETM 24- OLI) were used because they clearly distinguish the boundaries between land and water. The Digital Shoreline Analysis System (DSAS) was used to understand shoreline changes such as erosion and accretion processes by calculating the net shift in shoreline (NSM), end point rate (EPR), and linear regression rate (LRR) for the period of 25 years. The results showed significant changes over the studied period, leading to erosion and accretion. Coastal processes had an impact in the period from 2005 to 2014, leading to the development of a new island in the northern part of the wetland due to the construction of a new breakwater. In addition, the causes of the shoreline changes on the islands, including anomalies and the formation of new structures in the wetland, were investigated and discussed. These analyses confirmed that the islands studied were subjected to marine transgression and vice versa. Among this, Kavvayi was subject to marine transgression over the period, and Achanthurti Island showed erosion to a lesser extent. This can be considered as a feature of wetlands in combating sea level rise at local levels and possible reasons for the changes along with the coastal zone management plans were also discussed.

The contamination of water surfaces by mercury is a dangerous environmental problem due to its toxicity, which leads kidney damage. Activated carbon from mixed recyclable waste modified by phosphonium-based ionic liquid (IL-ACMRW) was therefore prepared and evaluated for Hg(II) remediation. The activated carbon used in this study was prepared from mixed waste, including cardboard, papers and palm wastes as cheap raw materials. The mixed Recyclable Waste Activated Carbon was combined with trihexyl(tetradecyl)phosphonium Bis2,4,4-(trimethylpentyl)phosphinate (Cyphos^® IL 104) ionic liquid to form an adsorbent with organic-inorganic content, in order to improve the Hg(II) uptake from aqueous solutions. FTIR confirms the presence of P, C=O and OH after this modification. The adsorption process was investigated and the evaluated results showed that the capacity was 124 mg/g at pH 4, with a contact time of 90 min, an adsorbent dose of 0.4 g/L, and a Hg(II) concentration of 50 mg/L. This Hg(II) adsorption capacity is superior than that reported in the literature for modified multiwall carbon nanotubes. The adsorption of Hg(II) on the modified activated carbon from mixed recyclable waste was found to follow the pseudo second-order kinetics model. Isotherms of adsorption were analyzed via Freundlich and Langmuir models. The results indicated that Freundlich is the best model to describe the process, suggesting multilayer adsorption.

The present research work focused on predicting the electrical conductivity (EC) of surface water in the Upper Ganga basin using four machine learning algorithms: multilayer perceptron (MLP), co-adaptive neuro-fuzzy inference system (CANFIS), random forest (RF), and decision tree (DT). The study also utilized the gamma test for selecting appropriate input and output combinations. The results of the gamma test revealed that total hardness (TH), magnesium (Mg), and chloride (Cl) parameters were suitable input variables for EC prediction. The performance of the models was evaluated using statistical indices such as Percent Bias (PBIAS), correlation coefficient (R), Willmott’s index of agreement (WI), Index of Agreement (PI), root mean square error (RMSE) and Legate-McCabe Index (LMI). Comparing the results of the EC models using these statistical indices, it was observed that the RF model outperformed the other algorithms. During the training period, the RF algorithm has a small positive bias (PBIAS = 0.11) and achieves a high correlation with the observed values (R = 0.956). Additionally, it shows a low RMSE value (360.42), a relatively good coefficient of efficiency (CE = 0.932), PI (0.083), WI (0.908) and LMI (0.083). However, during the testing period, the algorithm’s performance shows a small negative bias (PBIAS = − 0.46) and a good correlation (R = 0.929). The RMSE value decreases significantly (26.57), indicating better accuracy, the coefficient of efficiency remains high (CE = 0.915), PI (0.033), WI (0.965) and LMI (− 0.028). Similarly, the performance of the RF algorithm during the training and testing periods in Prayagraj. During the training period, the RF algorithm shows a PBIAS of 0.50, indicating a small positive bias. It achieves an RMSE of 368.3, R of 0.909, CE of 0.872, PI of 0.015, WI of 0.921, and LMI of 0.083. During the testing period, the RF algorithm demonstrates a slight negative bias with a PBIAS of − 0.06. The RMSE reduces significantly to 24.1, indicating improved accuracy. The algorithm maintains a high correlation (R = 0.903) and a good coefficient of efficiency (CE = 0.878). The index of agreement (PI) increases to 0.035, suggesting a better fit. The WI is 0.960, indicating high accuracy compared to the mean value, while the LMI decreases slightly to − 0.038. Based on the comparative results of the machine learning algorithms, it was concluded that RF performed better than DT, CANFIS, and MLP. The study recommended using the current month’s total hardness (TH), magnesium (Mg), and chloride (Cl) parameters as input variables for multi-ahead forecasting of electrical conductivity (EC_t+1, EC_t+2, and EC_t+3) in future studies in the Upper Ganga basin. The findings also indicated that RF and DT models had superior performance compared to MLP and CANFIS models. These models can be applied for multi-ahead forecasting of monthly electrical conductivity at both Varanasi and Prayagraj stations in the Upper Ganga basin.

A modified biological-integrated electrocoagulation method was explored to treat municipal wastewater (MWW) for irrigation purposes. To use treated wastewater for irrigation purposes a wide range of contaminants removal was focused on in this study (turbidity, hardness, conductivity, TDS, TSS, chloride, Ammonia nitrogen, BOD, COD, and total coliform). Raw municipal wastewater (RMWW) was treated in a modified Bio-Electrocoagulation (BEC) cell. The cell was operated in a continuous flow mode and consisted of an electrocoagulation stage using aluminum (Al) electrodes followed by a bioremediation stage using a fixed bio-filter (BF), the design of the cell was further modified by the addition of a sand filter (SF). The effect of several parameters such as applied voltage (22, 26, and 30 V), inlet flow rate (1, 3, and 5 Lh^-1), and initial pH (pH 3, 5, 7, 7.4, and 9) was investigated to determine the optimum operating conditions for selected responses. The most effective operating conditions for the BEC were investigated for the different irrigation water quality (WQ) indicators. It was observed that pH 7.4 and 26 V provide maximum removal efficiency of contaminants at the flow rate of 1 Lh^-1. A fixed film BF plays a positive role to improve the degradation of contaminants after the EC unit up to 4% of NH₃-N, 9.3% of BOD, and 7.8% of COD. In addition, using the SF improved the turbidity removal to 42.6%. The WQ specifications of the treated MWW using the BEC cell were compared with the standard specifications for restricted and unrestricted agricultural irrigation water. The overall operating cost of MWW treatment for irrigation purposes by using a modified bio-integrated electrocoagulation method was 0.76 $m^-3.

Little is known about how Indian farming practices affect German chamomile (Matricaria chamomilla L.). This study examines the effects of the sowing date and spacing of plants on flower productivity, essential oil concentration, and the composition of German chamomile grown in the arid zone of Rajasthan, India. In a factorial randomized block design (FRBD), the treatments consisted of four sowing dates (15 and 25 October, 5 and 15 November) and three spacings of plants (20 × 10 cm, 30 × 10 cm, and 40 × 10 cm). The dried flower yield (Kg ha⁻¹), fresh flower yield (Kg ha⁻¹), and number of flowers per plant of chamomile crop were significantly affected by the varying spacing of plants and the date of sowing. The highest values for dry weight, fresh weight, and number of flowers were obtained from the second date of sowing (25 October) with 40 × 10 cm geometry. Likewise, the highest values for total oil (12.44%) and essential oil (0.94%) contents were also obtained from the D2P3 combination (D2—sowing date 25 October, P3—40 × 10 cm spacing of plants). GC/MS analyses of the samples showed that p-menth-1-en-4-ol, acetate, cis-alpha-farnesene, anethole+estragol, 1,4-cyclohexadiene,1-methyl-4-(1-methylethyl)- and 3,6-dihydro-4-(4-methyl-3-pentenyl)-1,2-dithiin were the main identified compounds in the essential oil of chamomile fresh flowers. The treatments altered the quality profile of the essential oils in general. The principal components of chamomile essential oil were significantly affected by the D2P3 treatment. The findings of this study add to our understanding of how to grow high-quality chamomile flowers in arid regions.