This study evaluated the efficiency of methylene blue (MB) removal by using activated carbon produced from olive pits. The activated carbon (OPAC) was characterized by scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), and Brunauer–Emmett–Teller (BET). The adsorption process was optimized in two stages using factorial design. Based on the existing literature, the first stage selected the most influential variables (reaction time, dosage, pH, and dye concentration). Response surface methodology (RSM) and artificial neural network (ANN) approaches have been combined to optimize and model the adsorption of MB. To assess the optimal conditions for MB adsorption, RSM was initially applied using four controllable operating parameters. Throughout the optimization process, various independent variables were employed, including initial dye concentrations ranging from 25 to 125 mg/L, adsorbent dosages ranging from 0.1 to 0.9 g/L, pH values spanning from 1 to 9, and contact times ranging from 15 to 75 min. Moreover, the R² value (R² = 0.9804) indicates that regression can effectively forecast the response of the adsorption process within the examined range. Thermodynamic studies were performed for three different temperatures between 293 and 303 K. Isothermal analysis parameters and negative Gibbs free energy indicate that the process is spontaneous and favorable. The data best fit the Langmuir model. This research showcases the effectiveness of optimizing and predicting the color removal process through the combined RSM-ANN approach. It highlights the effectiveness of adsorption using OPAC as a viable primary treatment method for the removal of color from wastewater-containing dyes.

In this study, the effectiveness of Fe₃O₄-based clay as a cost-effective material for removing methylene blue (MB) dye from aqueous solutions was evaluated. The structural properties of the clay and Fe₃O₄-based clay were analyzed using SEM, XRF, BET, XRD, FTIR, and TGA techniques. In this research, the effects of various aspects, such as adsorbent amount, contact time, solution pH, adsorption temperature, and initial dye concentration, on the adsorption of Fe₃O₄-based clay are investigated. The experiments aimed at understanding the adsorption mechanism of Fe₃O₄-based clay have shown that the adsorption kinetics are accurately described by the pseudo-second order kinetic model, while the equilibrium data are well represented by the Langmuir isotherm model. The maximum adsorption capacity (qm) was calculated as 52.63 mg/g at 25 °C, 53.48 mg/g at 30 °C, and 54.64 mg/g at 35 °C. All variables affecting the MB adsorption process were systematically optimized in a controlled experimental framework. The effectiveness of the artificial neural network (ANN) model was refined by modifying variables such as the quantity of neurons in the latent layer, the number of inputs, and the learning rate. The model’s accuracy was assessed using the mean absolute percentage error (MAPE) for the removal and adsorption percentage output parameters. The coefficient of determination (R²) values for the dyestuff training, validation, and test sets were found to be 99.40%, 92.25%, and 96.30%, respectively. The ANN model demonstrated a mean squared error (MSE) of 0.614565 for the training data. For the validation dataset, the model recorded MSE values of 0.99406 for the training data, 0.92255 for the validation set, and 0.96302 for the test data. In conclusion, the examined Fe₃O₄-based clays offer potential as effective and cost-efficient adsorbents for purifying water containing MB dye in various industrial settings.

This study was aimed to evaluate the water quality and pollution sources in Sapanca Lake and its tributaries by applying multivariate statistical techniques to physicochemical parameters and toxic metals. For this purpose, the multivariate statistical methods such as principal component analysis (PCA) and absolute principal component score-multiple linear regression (APCS-MLR) model have been employed. It was tried to determine the seasonal pollution sources of physicochemical parameters and toxic metals obtained from 22 different sampling points between the years of 2015 and 2017. PCA was applied to the datasets, and 6 varimax factors describing 84, 80, 76, and 79 of the total variance for each season were extracted. The obtained factors were analyzed using the APCS-MLR model for the apportionment of various pollution sources affecting physicochemical parameters and toxic metals. The results show that the natural soil structure, municipal-industrial wastewater, agricultural-atmospheric runoff, highways, and seasonal effects are the major pollution sources for toxic metals and physicochemical parameters. The material contribution of pollutant sources to toxic metals and physicochemical parameters was calculated and verified by the concentrations analyzed. Consequently, multivariate statistical techniques are useful to determine the physicochemical parameters and toxic metals through reciprocal correlation and assess the seasonal impact of pollutant sources in the basin. This study also provides a basis for the creation of measurement programs, determination of pollution sources, and provision of sustainable watershed management regarding other water resources.

Sapanca Lake is an important drinking water source located by D-100 highway in the north and E-80 (TEM Anatolian Highway) and a railway line in the south. Heavy metal concentrations in roadside soils result from vehicle exhausts and the corroding metal parts of vehicles. Due to the difficulty of removing heavy metals from the soil, a significant pollution problem arises and this pollution also affects the water resources by means of rain. Although there are several industries, the highway located near the lake is the most important pollutant source for Sapanca Basin. Therefore, this study evaluated heavy metal concentrations, the chemical fractions of the metals and ecological risks (by using Cf, RAC and PERI) in the soil samples collected seasonally between 2015 and 2017 in Sapanca Lake Basin. Al and Fe were determined at very high amounts in all stations and the relationship between mean concentrations of other metals was determined to be Zn>Ba>As>Ni>Cr>Pb>Cu>Co>Cd. However, Zn, Ba, As, Ni, Cr, Pb, Cu, Co and Cd mean concentrations were determined as 87.63 mg/kg, Ba 86.87 mg/kg, 80.40 mg/kg, 58.62 mg/kg, 50.42 mg/kg, 41.90 mg/kg, 38.16 mg/kg, 13.98 mg/kg, 2.89 mg/kg respectively. Al, Fe, Co, Cr, Cu, Ni and Zn are mainly found in residual fractions in soil. According to the environmental risk assessment on the basis of soil stations, the 7th station has the highest GCf value, which means that the soil sample presents the highest environmental risk according to the contamination factors. On the other hand, Cd has serious potential ecological risk and As has considerable potential ecological risk in all stations.

This study explores the production of bio-nutrients from bioactive compound-rich spent coffee grounds (SCG) and biochar (BC) through composting after inoculation with a biological agent and its impact on the growth performance of garden cress and spinach. The SCG was composted with six doses of BC (0, 5, 10, 15, 20, and 25%). The compost with 10% BC exhibited the best maturity, humification, and phytotoxicity index values of dissolved organic carbon (DOC), humification index (E4/E6), and germination index (GI). A metagenome analysis showed that compost starter enhanced the bacterial community’s relative abundance, richness, and diversity in SCG and BC treatments. This improvement included increased Patescibacteria, which can break down noxious phenolic compounds found in SCG and BC. The BC enriched the compost with phosphorus and potassium while preserving the nitrogen. In plant growth experiments, the total chlorophyll content in compost-treated garden cress and spinach was 2.47 and 4.88 mg g−1, respectively, which was significantly greater (p ≤ 0.05) than in unfertilized plants and similar to the plants treated with traditional fertilizer. Overall, the results show that the compost of SCG + BC was well-suited for promoting the growth of garden cress and spinach, providing adequate nutrients as a fertilizer for these leafy vegetables.

In this study simultaneous analysis of seven different anions (Fluoride, Chloride, Nitrite, Bromide, Nitrate, Phosphate, and Sulfate) in the 13 different water sample collected from Sakarya/Turkey was conducted with ion chromatography method. Analyzes were performed simultaneously using the ion chromatography method. Some validation tests and the optimum conditions for the determination of anions were studied. The analysis of anions was accomplished by the dilution of the sample injection device. Samples were used to adjust the terms of the device and the results were recorded. Ion chromatography (IC) is now considered as an excellent technique for the analysis of ions in many samples.