Tunceli, Turkey is one of the small cities in the Eastern Anatolian Region with substantial natural, cultural and historical heritage that is suitable to be a slow city. Water sources are abundant in the area due to rain and heavy snowfall in the mountains, which seeps into the ground or flows into the rivers. Therefore monitoring and conservation of city’s drinking water quality is one of the main issues in the city. The present study was conducted to investigate several heavy metals and cyanide, arsenic, and selenium concentrations of drinking water samples taken from 100 different locations in the city and towns of Tunceli, Turkey during the years 2011 and 2012. In three locations, high levels of copper was found with a range of 2.1–11.5 mg/L, and high levels of arsenic was detected in five locations with a range of 10.5–78.0 μg/L, exceeding the action levels for copper and arsenic. For health risk assessment, the average daily dose and hazard quotient (HQ) were calculated. The region mean values of HQ were found above 1 for arsenic in the samples collected from Mazgirt, while HQ values were below 1 in the rest of the samples.

The compressive strength (Fc) of cement mortar (CM) is a key parameter in ensuring the mechanical reliability and durability of cement-based materials. Traditional testing methods are labor-intensive, time-consuming, and often lack predictive flexibility. With the increasing adoption of machine learning (ML) in civil engineering, data-driven approaches offer a rapid, cost-effective alternative for forecasting material properties. This study investigates a wide range of supervised linear and nonlinear ML regression models to predict the Fc of CM. The evaluated models include linear regression, ridge regression, lasso regression, decision trees, random forests, gradient boosting, k-nearest neighbors (KNN), and twelve neural network (NN) architectures, developed by combining different optimizers (L-BFGS, Adam, and SGD) with activation functions (tanh, relu, logistic, and identity). Model performance was assessed using the root mean squared error (RMSE), coefficient of determination (R²), and mean absolute error (MAE). Among all models, NN_tanh_lbfgs achieved the best results, with an almost perfect fit in training (R² = 0.9999, RMSE = 0.0083, MAE = 0.0063) and excellent generalization in testing (R² = 0.9946, RMSE = 1.5032, MAE = 1.2545). NN_logistic_lbfgs, gradient boosting, and NN_relu_lbfgs also exhibited high predictive accuracy and robustness. The SHAP analysis revealed that curing age and nano silica/cement ratio (NS/C) positively influence Fc, while porosity has the strongest negative impact. The main novelty of this study lies in the systematic tuning of neural networks via distinct optimizer–activation combinations, and the integration of SHAP for interpretability—bridging the gap between predictive performance and explainability in cementitious materials research. These results confirm the NN_tanh_lbfgs as a highly reliable model for estimating Fc in CM, offering a robust, interpretable, and scalable solution for data-driven strength prediction.

Heavy metals are persistent environmental contaminants that accumulate in soils and vegetation, posing significant risks to ecological systems and human health. Pinus nigra needles are widely recognized as effective biomonitors for reflecting spatial and temporal variations in atmospheric heavy metal deposition. However, the complex, nonlinear interactions among multiple pollutants, environmental factors, and site-specific conditions limit the effectiveness of conventional statistical approaches in accurately modeling and predicting contamination patterns. This study investigated the spatial and seasonal distribution of heavy metals in soils and Pinus nigra needles across different environmental settings in Adıyaman, Türkiye, including urban traffic zones, an organized industrial area, a cement factory vicinity, and a clean reference site. Metal concentrations were determined using inductively coupled plasma mass spectrometry (ICP–MS) following standardized acid digestion procedures. To address the limitations of traditional methods and capture complex nonlinear relationships, advanced machine learning (ML) algorithms—multilayer perceptron, Random Forest, XGBoost, LightGBM, CatBoost, and Gradient Boosting—were employed to model elevation based on heavy metal concentrations. The dataset was divided into training (80%) and testing (20%) subsets, and model performance was evaluated using R², RMSE, MAE, MAPE, and EVS. Among the models, XGBoost exhibited superior predictive performance. Excluding Cd, Cr, and Cu, it achieved R² = 0.9996 (RMSE = 0.068) in training and R² = 0.9526 (RMSE = 17.77) in testing. Including these metals further improved performance to R² = 0.9999 (RMSE = 0.054) for training and R² = 0.9890 (RMSE = 5.55) for testing. The results confirm that Pinus nigra needles are reliable bioindicators of heavy metal accumulation. More importantly, the integration of biomonitoring data with ML techniques provides a powerful framework for capturing complex environmental interactions and improving predictive accuracy, thereby supporting more effective environmental monitoring, risk assessment, and sustainable management strategies.

This study investigates the feasibility of utilizing locally sourced Nicotiana tabacum biomass from Adıyaman, Türkiye, as a cost-effective biosorbent for the removal of copper and chemical oxygen demand (COD) from industrial wastewater originating from the Adıyaman Organized Industrial Zone. Batch adsorption experiments were conducted to systematically investigate the influence of solution pH, contact time, and initial metal concentration on adsorption performance. The untreated wastewater exhibited elevated pollution levels, with mean chemical oxygen demand and copper concentrations of 925 ± 391 mg/L and 2.54 ± 0.97 mg/L, respectively. Four tobacco-derived biosorbents (Çelikhan, Ova, Bulam, and Çağlan) were evaluated under optimized experimental conditions (pH ≈ 8.3, 60 min contact time, and a biosorbent dosage of 2.2 g/L). The Çelikhan biosorbent exhibited the highest copper removal efficiency (approximately 83%), whereas chemical oxygen demand removal ranged between 28% and 34%. The adsorption kinetics were well described by the pseudo-second-order model, with coefficients of determination ranging from 0.987 to 1.000. Isotherm analysis further indicated favorable adsorption behavior, with a maximum Langmuir adsorption capacity of 1.867 mg/g. Fourier transform infrared (FT-IR) spectroscopy confirmed the involvement of hydroxyl, carbonyl, and ester functional groups in metal binding. These findings highlight tobacco biomass as a sustainable and cost-effective biosorbent for industrial wastewater treatment. This study presents the first comprehensive evaluation of locally sourced Adıyaman tobacco biomass as a biosorbent for the removal of copper and organic pollutants from real industrial wastewater, integrating kinetic, isotherm, and FT-IR analyses to elucidate the underlying adsorption mechanisms.

Particulate matter (PM) is one of the principal components of industrial air pollution due to its respirable size range and its ability to adsorb and transport other chemical pollutants, posing significant risks to both human health and the environment. Present in both solid and liquid forms, PM exerts effects on local, regional, and global scales. In this study, industrial flue gases were systematically monitored for dust concentrations and emissions, along with key operational parameters such as flow velocity, temperature, and humidity. The measurement results were analyzed to determine the interactions among these parameters and to establish optimized conditions for representative dust sumpling. This study indicates that flue gas temperature plays a critical role in the representativeness of dust sampling. At low temperatures (≈16–50 °C), the observed fluctuations reflect the inherent differences between stacks rather than measurement uncertainty, ensuring high representativeness. In the medium range (≈50–100 °C), both process stability and sampling reliability are largely maintained, allowing for consistent measurements. At high temperatures (>100 °C), however, dust concentrations appear lower and more stable, which may result from particle volatilization and moisture effects, suggesting potential underestimation of true emissions.