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...     

Batch-test study on the dechlorination of 1,1,1-trichloroethane in contaminated aquifer material by zero-valent iron

Chlorinated aliphatic hydrocarbons are common groundwater contaminants. One possible remediation option is in-situ reductive dechlorination by zero-valent iron, either by direct injection or as reactive barriers. Chlorinated ethenes (tetrachloroethene: PCE; trichloroethene: TCE) have received extensive attention in this context. However, another common groundwater pollutant, 1,1,1-trichlorethane (TCA), has attracted much less attention. We studied TCA reduction by three types of granular zero-valent irons in a series of batch experiments using polluted groundwater, with and without added aquifer material. Two types of iron were able to reduce TCA completely with no daughter product concentration increases (1,1-dichloroethane: DCA; chloroethane: CA). One type of iron showed slower reduction, with intermediate rise of DCA and CA concentrations. When evaluating the formation of daughter products, the tests on the groundwater alone showed different results than the groundwater plus aquifer batches: DCA did not temporarily accumulate in the batches with added aquifer material, contrary to the batches without added aquifer material. 1,1-dichloroethene (DCE, also present in the groundwater as an abiotic degradation product of TCA) was also reduced slower in the batches without added aquifer material than in the batches with aquifer material. Redox potentials gradually decreased to low values in batches with aquifer material without iron, while the batches with groundwater alone maintained a constant higher redox potential. Either adsorption processes or microbiological activity in the samples could explain these phenomena. Polymerase Chain Reaction (PCR: a targeted gene probe technique) for chlorinated aliphatic compound (CAH)-degrading bacteria confirmed the presence of Dehalococcoides sp. (chloroethene-degraders) but was negative for Desulfobacterium autotrophicum (a known co-metabolic TCA degrader). DCA reduction was rate determining: first-order half-lives of 300-350 h were observed. TCA was fully removed within hours. CA is resistant to reduction by zero-valent iron but it is known to hydrolyze easily. Since CA did not accumulate in our batches, it may have disappeared by the latter mechanism or it may not have formed as a major daughter product.     

Airborne organic micropollutant concentrations in mosses and humus as indicators for local versus long-range sources

The concentrations of HCB, PCB, and PAH in epiphytic mosses (Hylocomium splendens and Pleurozium schreberi) and forest humus were used to study the atmospheric exposure of conferous forests in a region in central Sweden. Air concentrations of HCB and PCB were monitored around an expected source. Moss contamination was used as a short-term exposure indicator, and humus contamination was assumed to indicate long-term exposure to HCB, PCB, and PAH.The level of HCB in mosses (0.4–1.7 ng/g, by dry weight, DW) complied well with background values. The levels of PCB measured as a sum of 7 identified PCB-congeners varied between 2–28 ng/g DW, with the highest levels in moss from the western part of the region, and decreasing concentrations from west to east. Around an expected PCB source in the southeastern part of the region the concentrations of PCB declined with increasing distance from the source area. The identified PAH concentrations in mosses varied between 39 and 730 ng/g DW. The PAH content in the mosses consisted mainly of nonvolatile PAH while the more volatile PAH were below the detection limit. The levels of HCB in humus were slightly more than in mosses (0.4–3.3 ng/g DW), and complied well with the background values. Sum PCB in the humus varied between 2 and 28 ng/g DW. The PAH in the humus were mainly nonvolatile PAH. The PAH concentrations in humus varied between 99 and 2600 ng/g DW. The air concentrations of PCB around the expected source (radius <1.5 km) were higher than background levels, and most likely caused the raised levels of PCB measured in the moss nearby.     

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...