The amount of medical waste is anticipated to increase significantly with population growth. Ineffective medical waste management has resulted in adverse impacts on environmental and human health. Therefore, this study aimed to develop the current medical waste management strategy in Istanbul. GaBi Education version 7.3 was used to conduct a life cycle assessment (LCA) to compare the current medical waste management system (baseline scenario) with alternative scenarios including different proportions of waste disposal methods from an environmental perspective. Global warming, acidification, eutrophication, ozone layer depletion, freshwater aquatic ecotoxicity, and human toxicity were selected as the environmental impact categories found in CML 2001 within GaBi software. Scenarios with a higher proportion of incineration had more negative environmental impact, whereas the scenario incorporating waste segregation/minimization contributed to reducing the environmental impact. Therefore, Scenario 4 (waste segregation at the generation points/waste minimization + incineration + steam sterilization + landfill) presented the best environmental performance with the lowest total environmental impact value of 14.21% among all scenarios and was recommended as the most sustainable alternative for medical waste management in İstanbul.

Many treatment methods are used to remove iron and manganese from water. Aeration and membrane filtration are two of these methods. In this study, Fe²⁺ and Mn²⁺ removal by aeration with different catalysts and instead of simple membrane filtration applied in other studies, the aerated-submerged membrane systems were evaluated separately. When Fe(OH)₃ was applied in the aeration step and complete oxidation of Fe²⁺ was obtained after 27 min, while complete Mn²⁺ oxidation was obtained in 76 min. However, when MnO₂ was applied in the aeration step, complete oxidation of Fe²⁺ and Mn²⁺ was relatively slow (36 and 110 min, respectively). According to the results obtained from the aerated membrane system, Fe²⁺ and Mn²⁺ removal were extended by Fe(OH)₃ via adsorption/surface oxidation. It is clearly shown from the flux, resistance results, scanning electron microscope (SEM) and Fourier transform infrared (FT/IR) spectroscopy observation that manganese oxides were deposited mainly in membrane pores forming membrane fouling by small flocs, while iron oxide particles were deposited on the membrane surface. Although the flux performance of PT PES membrane was higher than HF PP membrane, fouling resistance of HF PP membrane was higher than PT PES.

Due to its high oil content, the discharge of bilge water from ships is one of the most important pollutants in marine ecosystem. In this research, we investigated the treatment of bilge water for Haydarpasa Waste Collection Plant by Fenton oxidation followed by granular activated carbon (GAC) adsorption. We applied the following optimum operational conditions for Fenton oxidation: [Fe²⁺]: 6 mM; [H₂O₂]: 30 mM; and the ratio of [Fe²⁺]/[H₂O₂]: 1/5. Adsorption was performed in the effluent sample of Fenton oxidation. The effects of different adsorption periods, adsorbent concentrations, temperature, and pH were examined. Additionally, Freundlich and Langmuir isotherm models were applied. We obtained the following optimum operational conditions: 24 h, 2 g of GAC L⁻¹, 20 °C, and pH = 6. We observed an 89.5 ± 1.9% of Chemical Oxygen Demand (COD) removal efficiency under these conditions. Data generated from the experiments fit both isotherm models well, though we preferred the Langmuir isotherm model to the Freundlich isotherm model because the former’s regression coefficient (0.90) was larger than that reported for the Freundlich isotherm model (0.78). The potential to treat bilge water by Fenton oxidation followed by granular activated carbon is promising for the Haydarpasa Waste Collection Plant.

Effective medical waste management is crucial for minimizing environmental contamination, protecting occupational health, and advancing sustainability goals in healthcare systems. However, microbiology laboratories remain underexplored in waste characterization studies, despite their potential to contribute to sustainable healthcare operations. This study assessed waste generation patterns, classification accuracy, and the impact of training on regulatory compliance in a university hospital microbiology laboratory. Over 45 days, waste from six specialized units was categorized and weighed daily. A survey of 304 healthcare professionals evaluated their knowledge of medical waste handling. Statistical analyses revealed that training frequency (R² = 0.72, p < 0.01) was the most significant predictor of compliance, while years of experience had no measurable impact. On average, the laboratory generated 22.78 kg/day of medical waste, 11.67 kg/day of liquid waste, and 5.61 kg/day of sharps waste, with the bacteriology unit being the largest contributor. Despite adequate general awareness, 15% of staff misclassified hazardous waste—particularly expired pharmaceuticals and cytotoxic vials—indicating critical gaps in practice. The findings support the need for recurring training programs, stricter monitoring systems, improved waste labeling, and the integration of digital tracking tools. These interventions can reduce environmental burdens, enhance healthcare sustainability, and support the development of more resilient waste management systems in medical institutions. Future research should explore how AI and automation can further strengthen sustainable healthcare waste strategies.

This study presents the long-term performance evaluation of a full-scale hybrid membrane bioreactor (MBR)–nanofiltration (NF) system for the treatment of high-strength municipal landfill leachate from the Istanbul–Şile Kömürcüoda facility. Over a 16-month operational period, influent and effluent samples were analyzed for key parameters, including chemical oxygen demand (COD), ammonium nitrogen (NH₄⁺-N), total phosphorus (TP), suspended solids (SS), and temperature. The MBR unit consistently achieved high removal efficiencies for COD and NH₄⁺-N (93.5% and 98.6%, respectively), while the NF stage provided effective polishing, particularly for phosphorus, maintaining a TP removal above 95%. Seasonal analysis revealed that the biological performance peaked during spring, likely due to optimal microbial conditions. To support intelligent control strategies, artificial neural network (ANN) models were developed to predict effluent COD and NH₄⁺-N concentrations using influent and operational parameters. The best-performing ANN models achieved R² values of 0.861 and 0.796, respectively. The model’s robustness was validated through RMSE, MAE, and 95% confidence intervals. Additionally, Principal Component Analysis (PCA) and Random Forest algorithms were employed to determine the parameter importance and nonlinear interactions. The findings demonstrate that the integration of hybrid membrane systems with AI-based modeling can enhance treatment efficiency and forecasting capabilities for landfill leachate management, offering a resilient and data-driven approach to sustainable operation.