Stabilization of metal processing plant sludge via sequential application of phytoremediation and pyrolysis

The aim of this study is to evaluate the metal removal ability of three different plants from metal processing sludge containing Pb, Cd, and Zn. Therefore, phytoremediation and pyrolysis were sequentially applied. In the phytoremediation applications, sunflower (Helianthus annuus), corn (Zea mays), and rape (Brassica napus) seeds were sown in sludge/soil mixtures at four different levels (25/75, 50/50, 75/25, 100/0). The chelating agent, ethylenediaminetetraacetic acid, was added to the mixtures for plant uptake during phytoremediation. At the phytoremediation stage, it was noted that rape was the most effective plant for the mixture of 75/25 sludge/soil, with metal removal efficiencies ranging between 80%–90%. At the pyrolysis stage, after harvesting, contaminated plants grown in a 75/25 sludge/soil mixture were pyrolyzed at 500 °C, with a heating rate of 35 °C/min. The results show that 60%–90% of the initial metal content was held by the solid product. In addition to this, it can be concluded that pyrolysis stabilizes metals into a solid product and that this solid product can be safely landfilled as inert waste since its toxicity leaching value is lower than the limit values.     

Hepatic effects of yttrium oxide nanoflowers: in vitro risk evaluation

Yttrium oxide nanoflowers were prepared by a hydrothermal technique, and X-ray diffraction and scanning electron microscopy were used to determine their structures. The cytotoxic and genotoxic potentials of aqueous dispersions of the nanoflowers to cultured primary rat hepatocytes were examined at concentrations up to 500 mg L^?1 for 72 h. Cell viability was determined by monitoring the reduction of 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide, release of lactate dehydrogenase, and uptake of neutral red. Genotoxicity was assessed by the liver micronucleus assay. Exposure to Y₂O₃ nanoflowers at concentrations lower than 100 mg L^?1 did not lead to any cytotoxicity or genotoxicity. At higher concentrations (200, 400, and 500 mg L^?1), cell viability decreased and induction of micronuclei increased (400 and 500 mg L^?1).     

An Investigation into the Influence of C. moschata Leaves Extract on Physicochemical and Biological Properties of Biodegradable PCL/PLA Blend Film

Journal of Polymers and the Environment - Poly ε-caprolactone (PCL) synthesized by ring-opening polymerization method, and then it blended with polylactic acid (PLA). The blend was loaded with...     

Impacts of mycotoxin on biohydrogen production from waste dry fruits

Journal of Material Cycles and Waste Management - Discarded dry fig and raisin, which exporting companies have to claim and destroy, were subjected to batch dark fermentation at 37 °C...     

Towards zero-waste airports: a case study of Istanbul Airport

Journal of Material Cycles and Waste Management - In this day and age, an important indicator of sustainable waste management is zero-waste index. Zero-waste approach is adopted by many...     

Comparison of hybrid machine learning methods for the prediction of short-term meteorological droughts of Sakarya Meteorological Station in Turkey

Drought is a harmful natural disaster with various negative effects on many aspects of life. In this research, short-term meteorological droughts were predicted with hybrid machine learning models using monthly precipitation data (1960–2020 period) of Sakarya Meteorological Station, located in the northwest of Turkey. Standardized precipitation index (SPI), depending only on precipitation data, was used as the drought index, and 1-, 3-, and 6-month time scales for short-term droughts were considered. In the prediction models, drought index was predicted at t?+?1 output variable by using t, t???1, t???2, and t???3 input variables. Artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), Gaussian process regression (GPR), support vector machine regression (SVMR), k-nearest neighbors (KNN) algorithms were employed as stand-alone machine learning methods. Variation mode decomposition (VMD), discrete wavelet transform (DWT), and empirical mode decomposition (EMD) were utilized as pre-processing techniques to create hybrid models. Six different performance criteria were used to assess model performance. The hybrid models used together with the pre-processing techniques were found to be more successful than the stand-alone models. Hybrid VMD-GPR model yielded the best results (NSE?=?0.9345, OI?=?0.9438, R²?=?0.9367) for 1-month time scale, hybrid VMD-GPR model (NSE?=?0.9528, OI?=?0.9559, R²?=?0.9565) for 3-month time scale, and hybrid DWT-ANN model (NSE?=?0.9398, OI?=?0.9483, R²?=?0.9450) for 6-month time scale. Considering the entire performance criteria, it was determined that the decomposition success of VMD was higher than DWT and EMD.

     

Solid waste composition and COVID-19-induced changes in an inland water ecosystem in Turkey

Environmental Science and Pollution Research - The composition and abundance of solid waste and the effect of COVID-19 measures were studied in an inland water ecosystem in Turkey. Solid waste...