Berna Kırıl Mert

The textile industry causes significant environmental problems because of its intensive use of water, energy, and chemicals. The stenter machines, which are commonly used in textile finishing processes, release air pollutants such as odor, volatile organic compounds (VOCs), and total organic carbon (TOC) into the atmosphere during drying and fixing processes carried out at high temperatures. The aim of this study was to investigate odor, VOC, and TOC emissions from the stacks of the stenter machines. In this study, odor, VOC, and TOC parameters were examined in samples from the stacks of stenter machines of nine different plants operating in the textile sector in Bursa, Turkey. The samples were analysed in accordance with EN 13725:2022 standard, EN 13649:2014 standard, and EN 12619:2013 standard for odor, VOC, and TOC parameters, respectively. Acetone, carbon tetrachloride, dibromochloromethane, ethylbenzene, tetrachlorethylene, toluene, and p + m-Xylene were the most common components. The TOC concentrations were determined in the range of 13.89–279.23 mg/Nm³. The odor concentrations were determined in the range of 4113–26,627 OU/m³.

Fireworks are widely used at festive events worldwide and are commonly employed at indoor wedding ceremonies in Türkiye; however, their impact on indoor air quality has not been adequately investigated. This study examines particulate matter (PM) emissions generated by volcano-type fireworks used in indoor wedding halls. Particle samples were collected across five size fractions (>2.5, 1.0–2.5, 0.50–1.0, 0.25–0.50, and 2.5 concentrations ranged between 1518 and 7796 µg/m³, while total PM concentrations varied between 2088 and 8656 µg/m³. These values are substantially higher than the guideline limits reported by the World Health Organization (WHO) and the United States Environmental Protection Agency (USEPA), indicating very high short-term exposure levels. Fine particles (PM_2.5) accounted for 56.5–92.4% of total PM, with particles smaller than 1 µm forming the dominant fraction. Among the analyzed elements, aluminum (Al), zinc (Zn), and iron (Fe) were the most abundant metals, predominantly associated with fine particles. The highest concentrations were generally observed in the 0.25–0.5 µm size range. These findings showed that the use of indoor fireworks can lead to high concentrations of fine particulate matter and metal-rich aerosols. This issue should be considered, as it may pose a health risk for those in enclosed spaces in future studies.

This study analyses the effects of operating conditions on the removal of three different pharmaceutically active compounds (PhACs: paracetamol [PARA], diclofenac [DIC] and metoprolol [MET]). The results of the experiments employing nanofiltration (NF) membranes were interpreted using response surface methodology (RSM) analysis of variance (ANOVA). In experiments performed with NF270 membrane under optimal operating conditions (15 bar pressure, 4 volume reduction factor [VRF], 5.7 pH), removal efficiencies of PhACs were 11.5%, 73.8% and 46.8%, respectively. The design of NF processes can help substantially in determining the rejection behaviours and mechanisms of micropollutants. Experiments have shown that in drug active substances removal with NF membranes, the physicochemical properties of the NF membranes used and the compound being PARA, DIC or MET affect the removal efficiency and provide an understanding of the rejection performance. These factors may also provide a guide for the applicability of the NF technology in advanced treatment processes in the future.

Solid wastes have become one of the most critical problems in urban areas because of the increase in the amount and the environmental problems caused by the variety of these wastes. In the same local governments where waste management is mismanaged, much money is wasted, and landfills are unnecessarily occupied. Making a profit out of solid wastes, seen as a heavy burden to the world and our country, aims firstly to collect these wastes apart from their sources by characterizing them. Recycling of both household and industrial wastes should be planned and done at the source. Collection is the most laborious and costly element of solid waste management. The study area was divided into four groups according to income levels, and the waste generated there was evaluated. The study showed that for 2022, kitchen waste ranks first with 67.25%. Kitchen waste is followed by packaging waste at 21.03%; flammable wastes (textile etc.) at 21.04%; ash waste at 44%, While hazardous wastes have a share of 0.55%; lastly, park and garden wastes and electronic wastes were not detected at all. It is seen that waste management activities are carried out in the Çayırova region, but the integration of citizens into waste management activities needs to be increased. In this regard, improvement efforts have been made to increase efficiency in separating collection from the source and reduce the amount of final waste.

Artificial neural networks offer a viable route in assessing and understanding the presence and concentration of heavy metals that can cause dangerous complications in the wider context of water quality prediction for the sustainability of the ecosystem. In order to estimate the heavy metal concentrations in Iznik Lake, which is an important water source for the surrounding communities, characterization data were taken from five different water sources flowing into the lake between 2015 and 2021. These characterization results were evaluated with IBM SPSS Statistics 23 software, with the addition of the lake water quality system. For this purpose, seven distinct physicochemical parameters were measured and monitored in Karasu, Kırandere, Olukdere and Sölöz water sources flowing into the lake, to serve as input data. Concentration levels of 15 distinct heavy metals in Karsak Stream originating from the lake were as the output. Specifically, Sn for Karasu (0.999), Sb for Kırandere (1.000), Cr for Olukdere (1.000) and Pb and Se for Sölöz (0.995) indicate parameter estimation R² coefficients close to 1.000. Sn stands out as the common heavy metal parameter with best estimation prospects. Given the importance of the independent variable in estimating heavy metal pollution, conductivity, COD, COD and temperature stood out as the most effective parameters for Karasu, Olukdere, Kırandere and Sölöz, respectively. The ANN model emerges as a good prediction tool that can be used effectively in determining the heavy metal pollution in the lake as part of the efforts to protect the water budget of Lake Iznik and to eliminate the existing pollution.

Since high levels of heavy metals cause serious complications for water resources, plants, animals and human health, determining their presence and concentration is very important for the sustainability of the ecosystem. In recent years, rapid advances in the field of artificial neural networks (ANNs) brought them the forefront in water quality prediction. In this paper, various experiments were conducted with a model for predicting the presence of heavy metals using IBM SPSS statistics 23 software. In order to assess the water quality of Lake Iznik –an important source of water– in terms of heavy metals, water quality parameters of samples taken in the period 2015–2021 from five different water sources flowing into the lake were analyzed. A number of psychochemical were measured in samples taken from Karasu, Kırandere, Olukdere, and Sölöz streams flowing into the lake, and were used as input data for modeling, while fifteen heavy metal concentrations in Karsak stream flowing out of the lake were used as output data of the model. The analyses showed that the R² coefficients for heavy metals were mostly close to 1. Considering the importance of the independent variable in heavy metal pollution prediction, the most effective parameters for streams stood out to be conductivity, COD, COD, and temperature, respectively. It was seen that ANN model is a good prediction tool method that can be used effectively to determine heavy metal pollution in the lake in terms of ecological sustainability in order to conservation the water quality of Lake Iznik and to eliminate the existing pollution.

Active substances of drugs can cause various adverse effects by accumulating in the ecosystem. Many medications are resistant to biodegradation, given the recipient media in conventional wastewater treatment plants, and are thus released into the environment after only partial purification or no purification at all. The study focuses on 13 different pharmaceutical compounds belonging to drug classes of anti-depressants, antiepileptic’s, anti-inflammatories, beta-blockers, lidocaine, and stimulants. These compounds were selected with reference to the literature as the ones most commonly encountered in domestic wastewater, surface, and groundwater. The presence of these compounds in the wastewater samples from Sakarya sewage and wastewater treatment plant was investigated. For this purpose, composite samples were taken at various sampling points, and duly analyzed. The analysis revealed the presence of the pharmaceutical residues in the sewage waters from Sakarya Municipality. Some of them were still present in the effluent of the treatment plant. On the other hand, fluoxetine, propranolol, and metoprolol drug active ingredients were not detected at any sampling point. Among all the compounds examined, the highest percentage of residues were observed in the case of active caffeine and paracetamol. In the light of these findings, advanced treatment units such as high-pressure membrane systems (including ozonizing, ultrafiltration, and reverse osmosis) can help adsorption rates at the treatment plant, increasing removal efficiency regarding drug compounds.