A high pH, low solubility of bound plant nutrients, and negative impacts on microbial communities are common drawbacks of biomass ash (BA) vermicomposting. In this study, nutrient-rich BA mixed with cow manure was tested at three different application rates to obtain final nitrogen (N), phosphorus (P), and potassium (K) contents of 3.5%, 7.0%, and 10.0% for bio-based fertilizers via vermicomposting. The results showed that all BA blends made with cow manure increased fermentation temperatures and allowed successful worm activity during the subsequent vermicomposting phase. The order of indicator enzyme activities in all vermicomposting samples was urease (220 μg NH₄ g⁻¹ h⁻¹) > β-glucosidase (95 μg PNP g⁻¹ h⁻¹) > alkaline phosphatase (91 μg PNP g⁻¹ h⁻¹) > arylsulfatase (83 μg PNP g⁻¹ h⁻¹) > acid phosphatase (60 μg PNP g⁻¹ h⁻¹). As an indicator of nutrient bioavailability, high correlations were observed between enzyme activities and microbial diversity in vermicompost samples. Determination coefficients (R²) obtained from multiple linear regressions between enzyme activities and bacterial population for T₀, T₁, T₂, and T₃ were determined as 0.90, 0.65, 0.73, and 0.90, respectively. According to a novel metagenome-based approach proposed within the scope of the present study, the stimulatory effects of Flavobacteriales, Burkholderiales, Saccharimonadales, and Pseudomonadales on enzyme activities for the nutrient solubility were found to be significant and positive. The findings of this study demonstrated that worm composting could be a sustainable bio-based technology for the production of slow-release fertilizer from nutrient-rich waste material.

In this study, polysulfone/polyvinylpyrrolidone (PSf/PVP, 20 wt%/5 wt%)-based ultrafiltration (UF) membranes reinforced with different ratios (0.5 and 1 wt%) of cellulose nanocrystals (CNCs) and cellulose nanofibres (CNFs) were prepared by the phase inversion method. The effect of CNC, CNF, and CNC-CNF reinforcement on the morphology, roughness, crystallinity, porosity, average pore size, mechanical properties, and filtration performance of PSf/PVP-based membrane was investigated. Distilled water and surface water (lake water) fluxes of the membranes were determined at 3 bar using a dead-end filtration system. The distilled water flux of the fouled–hydraulic cleaned membranes was determined, and scanning electron microscopy (SEM) images of the fouled–cleaned membranes were examined. The flux recovery ratio (FRR) and fouling parameters were calculated to examine the fouling behaviour of the membranes. The mechanical properties of the membranes were modelled by the Mori–Tanaka, finite element, Voigt–Reuss, self-consistent scheme, and Halpin–Tsai methods using Digimat and/or analytically. In addition, the von Mises equivalent stress distributions of the nanocomposites were presented. Among the investigated membranes, PSf/PVP/CNC-0.5 had the highest distilled water flux (475.5 ± 17.77 L/m².h), PSf/PVP/CNF-1 exhibited the stiffest behaviour with an elasticity modulus of 70.63 ± 3.15 MPa, and PSf/PVP/CNC-1 had the best organic matter removal efficiency. The finite element was the most successful modelling method for estimating the mechanical properties of nanocellulose-reinforced flat sheet membranes.

The Riva River is a water basin located within the borders of Istanbul in the Marmara Region (Turkey) in the south-north direction. Water samples were taken for the 35 km drainage area of the Riva River Basin before the river flows into the Black Sea at 4 stations on the Riva River every month and analyses were carried out. Changes were observed in the quality of water from upstream to downstream. For this purpose, the spatial and temporal variations of water quality were investigated using 13 water quality variables with the ANOVA test. It was observed that COD, DO, S and BOD were important in determining the spatial variation. On the other hand, it was found out that all the variables were effective in determining the temporal variation. Moreover, the correlation analysis which was carried out in order to assess the relations between water quality variables showed that the variables of BOD-COD, BOD-EC, COD-EC, BOD-T and COD-T were correlated and the regression analysis showed that COD, TKN and NH₄-N explained BOD and BOD, NH₄-N, T and TSS explained COD by approximately 80 %. Consequently, the Artificial Neural Network (ANN), Decision Tree and Logistic Regression models were developed using the data of training set in order to predict the water quality classes of the variables of COD, BOD and NH₄-N. Quality classes were predicted for the variables by inputting the data of testing set into the developed models. According to these results, it was seen that the ANN was the best prediction model for COD, the Decision Tree for BOD and the ANN and Decision Tree for NH₄-N.

The responses of sludge faecal coliforms, Salmonella, and Ascaris lumbricoides to heat drying, solar dehydration, and inactivation in soil are examined in this study. The presence of Salmonella in raw sludge cake after treatment was low, and absent for most of the cases. Likewise, the viable Ascaris eggs were not determined because of absent or low prevalence. Faecal coliforms, on the other hand, drastically decreased from 4.2 × 10^7 MPN g^{-1} Dry Solid (DS) to absence by heat drying. Faecal coliform numbers in solar and non-solar drying treatments were not declined below 1.0 × 10^3 MPN g^{-1} after drying to 90% DS and also during the storage of 12 weeks. After 6 weeks, faecal coliform and Salmonella levels in the soil surface application fell below undetectable limits. The coliform numbers below soil surface fell gradually over 12 weeks and numbers were below undetectable limits at end of the experiment. These results showed that although the solar drying has significant effects on removing pathogenic microorganisms during hot and dry periods, the soil treatment was still necessary for the final complete inactivation. Solar exposure on the soil surface was found to be effective on the elimination of pathogens.

The textile industry is an industry that consumes large amounts of water during production, contains various chemicals in its wastewater, conventional treatment methods are insufficient to reduce the wastewater pollution level, and has colloidal substances and color problems. Membrane bioreactor systems provide high efficiency in the treatment of textile wastewater and dyestuff removal. Removal of dyestuffs and turbidity in real textile wastewater by using a laboratory-scale membrane bioreactor system was studied. Chemical precipitation was not applied before the biological treatment for the removal of color and other pollutant parameters. A hollow fiber microfiltration membrane module was used in the system. Then a combination with an active carbon filter was created to take the color removal to a higher level. The development of the microorganism composition adapted to the textile industry was observed in the biological reactor. The system was operated with an endless sludge age and a hydraulic retention time of 24 hours. Color measurement transparency index parameter DFZ (DurchsichtsFarbZahl) was measured in a spectrophotometer at wavelengths of 436, 525, and 620 nm (nanometers) according to EN ISO 7887 standards. In the microfiltration permeate water, the color removal were found in 436 nm: 91-95%, 525 nm: 94-98%, 620 nm: 96-99%, and in activated carbon permeate water, the color removal in 436 nm: 96-99% at 525 nm: 95-99%, 620 nm: 96-99%, respectively. Due to the physical separation of the membrane, which is the simplest definition, high efficiencies in color removal have been achieved in the system. The activated carbon system combined with the membrane was found higher efficiency in color removal than the microfiltration output.