Yusuf Alparslan Argun

This study examines the biosorption of Methylene Blue (MB), Basic Blue 41 (BB41), Reactive Red 120 (RR120), Methyl Red (MR), and Trypan Blue (TB) dyes, commonly used in the textile industry, using Juniperus drupacea cone as an biosorbent. The effects of biosorption time, initial dye concentration, temperature, pH, and particle size on dye removal efficiency were investigated. Characterization techniques such as SEM-EDX, FTIR, isotherm, kinetic, thermodynamic, and intraparticle diffusion analyses were performed. The Langmuir isotherm model indicated monolayer biosorption for MB and BB41, whereas the Freundlich isotherm suggested heterogeneous biosorption for RR120 and MR. The biosorption process followed the pseudo-second-order kinetic model, highlighting the dominance of chemical interactions. Thermodynamic analysis confirmed that MB and BB41 biosorption was spontaneous, while MB biosorption was exothermic. Intraparticle diffusion analysis suggested that biosorption was not solely controlled by intraparticle diffusion but also influenced by surface biosorption. The highest removal efficiency was recorded as 97.77% for MB under optimal conditions (pH 10, 55 °C, 75 μm biosorbent size). BB41 exhibited a maximum removal efficiency of 92.63%, with increasing biosorption at higher temperatures. The results demonstrate that Juniperus drupacea cone is an efficient and environmentally sustainable biosorbent for dye removal from wastewater. The study contributes to sustainable wastewater treatment technologies and offers a promising alternative for valorizing underutilized plant materials. These findings support the use of low-cost biosorbents in environmental applications and provide a foundation for future research on industrial-scale implementation.

Air pollution is a global environmental problem that poses serious threats to human health and quality of life, especially in industrializing and densely populated areas. The use of machine learning techniques in air pollution prediction has become an important tool in air quality management. In this study, PM10 concentrations were estimated using support vector machines (SVM), artificial neural networks (ANN), convolutional neural networks (CNN), random forest (RF), gradient boosting (GB), K ‐nearest neighbor (KNN), linear regression (Lasso), and long short‐term memory (LSTM) models, and the models were examined in detail. The results showed that the SVM model performed best with the lowest error values (mean absolute error [MAE]: 4.747 µg/m ³ and root mean square error [RMSE]: 6.648 µg/m ³ ), whereas the ANN model achieved the highest accuracy level ( R ² : 0.791). The KNN model showed the weakest performance with the highest error rates (RMSE: 10.07 µg/m ³ ). Among the LSTM models, the model trained with the Adam optimization algorithm exhibited the best performance compared to other LSTM versions. Among the models used in PM10 estimation, SVM, ANN, and CNN highlight the effectiveness of machine learning models for air pollution estimation and constitute an important reference for studies to be conducted in this field. Focusing on Karaman, a medium‐sized industrial city dominated by food‐processing industries, it provides a representative framework for similar medium‐sized industrial cities facing comparable environmental challenges. The research introduces an integrated and transparent machine‐learning approach that combines data preprocessing, outlier correction, normalization, and broad hyperparameter optimization under consistent conditions for eight algorithms. This framework improves the reliability, comparability, and reproducibility of air‐quality prediction.

The zero waste approach introduces a novel perspective on waste management, allowing for the categorization of waste at its origin. This enables each identified waste group to be assessed and optimized individually. One of these optimization studies focuses on the optimization of waste collection and transportation. The placement and number of containers, as well as the collection routes of vehicles, are crucial for optimizing these processes. In this study, the number of containers placed in Karaman, their placement, the distance between the containers, and the vehicle route optimization were analyzed. The results show that the current number of containers is not used efficiently, with an average distance of only 33 meters between them. Moreover, optimizing routes in five pilot areas resulted in an average efficiency gain of 32%, which saved 17 kilometers, 50 minutes, and 8 liters of fuel per route. Based on these findings, a 32% improvement is anticipated not only in economic and time efficiency but also in reducing the carbon footprint during the collection and transportation stages.

This study aims to determine the spatial distribution of heavy metal pollution in Ermenek Dam Lake, water quality assessment and pollution sources. For this purpose, samples were taken 6 times a year from 12 points determined in 2024. Physico-chemical parameters and heavy metals were analyzed in the study. Using the analysis results, indexes such as Water Quality Index and Heavy Metal Pollution Index were calculated. The results show that the lake water complies with TS 266 and WHO standards. The index results indicate that the lake water is at low pollution and risk level and is safe. The distribution and sources of heavy metals were examined using correlation analysis, Principal Component Analysis and Hierarchical Cluster Analysis. Correlation analyses showed that there were significant relationships between pH, temperature, conductivity, dissolved oxygen and TDS and heavy metals. PCA results revealed that Zn was positively correlated with temperature and pH, while Mn and Ni were inversely correlated. The alignment of Pb, Cu and Cd in the same direction showed that these parameters were affected by common sources. HCA results showed that Cr and Fe have similar transport and source properties, while Mn and Ni are affected by different sources. In general, it was determined that heavy metal pollution in lake water was at low levels and local concentrations were present. The study revealed the effects of anthropogenic activities on the lake ecosystem. The findings of the study provide a guiding basis for water quality management in similar hydrological systems. Graphical abstract

Climate change is a major concern around the world. In this context, the carbon footprint of animal waste is of critical importance for sustainability and climate change management. The aim of this study is to estimate the carbon footprint resulting from animal breeding and animal waste in the Karaman region. In the study, 2022 data was used and there are 1019277 sheep and 81368 cattle for Karaman. In the study, Tier 1 and Tier 2 approaches were used to estimate methane gas emissions related to both enteric fermentation resulting from animal digestive processes and manure management. According to the results of the study, the results obtained using Tier 1 and Tier 2 approaches are as follows: 0.4924-0.5014 kg CO2e per 1 liter of milk for Cattle. For sheep, it is 4.5167-6.5627 kg CO2e per 1 kg of meat. For goat, it is 5.0813-6.0231 kg CO2e per 1 kg of meat. These results can be taken into account when creating strategies to reduce methane gas emissions. It is recommended to add better quality and high energy content foods to the feed ration, especially to reduce enteric fermentation. In addition, this study is a resource for relevant researchers working in the field in calculating the carbon loads of animal waste and is thought to be a guide for decision makers and practitioners.

Animal wastes are not stored correctly and are used haphazardly without control in agricultural lands. As a result, it causes many irreparable environmental pollution, especially water pollution. These wastes, which are described as diffuse pollution, cause pollution of both underground and surface water resources directly or indirectly and even make them unusable. In this research, waste generation as a result of animal breeding in Karaman province, its districts, neighborhoods and villages and the effects of animal wastes on environmental pollution were evaluated with distributed pollutant load calculations. In the study, the number of 1019277 ovine and 81368 bovine in Karaman in 2022 was used. The total nitrogen (TN) produced annually by the animals has been calculated as 1,723.23 tons/year, and the total phosphorus (TP) amount is determined as 124.23 tons/year. Additionally, for large ruminant animals, the annual total amount of dry manure is 130,305.77 tons, and for small ruminant animals, it is 41,984.27 tons. To prevent environmental pollution, these wastes should be stored in closed areas in compliance with standards, and processes such as composting, drying, and biogas production should be applied. By doing so, not only can environmental pollution be mitigated but also economic value can be obtained. The proper management and utilization of these wastes have high economic potential and can contribute to sustainable development, supporting the country’s economy. In addition, this study is a source for researchers working in the field in calculating the pollution load of animal wastes and is thought to be a guide for decision makers and practitioners.

The most basic building block of solid waste management is to determine the amount of waste generated by individuals in a day. As it can be understood from here, the most important issue in solid waste management is individuals. Therefore, all kinds of waste management studies should be based on the individual. In our study, a questionnaire was applied to 1 263 people residing in Karaman. In addition, according to income levels, 5 pilot regions were determined and characterization studies were carried out in different time periods. Of the wastes generated as a result of characterization, 45.08% of kitchen wastes, 3.41% of park and garden wastes and 30.36% of recyclable wastes. Only 18.6% of the recyclable waste potential is collected with the existing system. Within the framework of the survey, the rate of those who collect recyclable waste separately is 21%. In addition, it is understood from the survey study that individuals do not know what type of waste is produced in their homes. In this framework, waste collection and evaluation suggestions were presented by determining the waste potentials with the characterization study and the individual attitudes with the survey study.

Toplumlardaki tüketim alışkanlıklarının değişen teknoloji ve refah seviyesi beklentilerinin her geçen gün değişmesi ile birlikte artmaktadır. Bu teknolojik gelişmeler ve rahat yaşam beklentileri üretilen ürünü ve miktarını arttırmaktadır. Bu da doğal kaynakların tüketim hızını doğrudan etkilemektedir. Ayrıca tüketim sonucunda oluşan atıklarda çevre sağlığı ve doğal kaynaklar açısından sorun oluşturmaktadır. Bu minvalde bakıldığında hem yeni ürün üretimi için doğal kaynak kullanımını azaltmak hem de tüketim sonucu oluşan atıkların çevresel etkilerini en aza indirmek için geri kazanım kaçınılmaz bir durumdur. Bu çalışmada Karaman’da oluşan kentsel katı atıkların kompost ve geri dönüşüm kapsamında değerlendirilme potansiyelleri incelenmiştir. Çalışmada, Karaman iline ait yıllık 68270 ton kentsel katı atık oluştuğu belirlenmiştir. Bunun %45,08’ini mutfak atıkları, %3,41’ni park ve bahçe atıkları ve %30,36’sını da geri dönüşebilir atıklar oluşturmaktadır. Karaman genelinde lisanslı tesis tarafından toplanan ambalaj atık miktarı günlük ortalama 10380kg olup, oluşan katı atıkların %5,6’sına tekabül etmektedir. Toplanan ambalaj atığı miktarının geri dönüşebilir atık miktarına oranı ise %18,6’dır. Karaman ilinde 1000 L/hafta kapasiteye sahip 7 adet otomatik kompost makinesi mevcut olup kompostlaştırma işlemi aktif olarak yapılmamaktadır. Sonuç olarak Karaman ilinin geri kazanım potansiyeli ciddiye alınması gereken bir değere sahip olup sıfır atık ilkelerine uygun bir şekilde yönetilmesi gerekmektedir.

In this study, it was examined effects of climatic and topographic conditions, urbanization and transportation on air quality of Igdir which is the eastern province of Turkey and is an agricultural city. An important part of the city's settlements is on fertile farmland. Air pollutants arising from industry, settlements and transportation show the density on the city center mostly without transcending elevations which have bowl shape and are located around the city during the winter months. Air pollution during the winter months causes a dense fog over the city for weeks under the influence of inversion layer and is reaching significantly amount to effect on human health. Igdir province is located in the orange group (it may occur health implications for vulnerable groups) in terms of EPA air quality index. Also, it is much higher than 80 µg m-3 which is national limit value for PM10. In addition, the average wind speed of Igdir is 1.2 m s-1 and has not enough power to distribute polluted air.