In this study, the concentrations and characteristics of copper (Cu), zinc (Zn), and lead (Pb) contamination in sediment samples were investigated using aqua regia extraction and Tessier’s five-step sequential extraction. Based on the concentration of metals, the influence of the Hapcheon-Changnyeong weir on sediments in the Nakdong River was assessed. The origins of the contaminants, their bioavailability, and their mobility were determined using sequential extraction. Greater concentrations of heavy metals were found in samples collected closer to the weir. The largest proportion of Cu was identified in the residual fraction based on sequential extraction, whereas Zn was predominantly found in the reducible fraction. Iron-manganese in the reducible fraction of Zn has the potential to leach back to the water body. In addition, the combined concentration of fractions 1 and 2 of Cu comprised more than 20% of total amount that still has potential to affect the water quality. The results of this study were compared with existing sediment standards set out by the NIER (National Institute of Environmental Research), Canada, and US EPA (United States Environmental Protection Agency) guidelines, as well as the risk assessment code (RAC). The concentrations of heavy metals exceeded the standards set by the Canadian guideline by up to four times in particular samples, highlighting the need for continual monitoring.
<p>River Water Quality (RWQ) is significantly influenced by natural and anthropogenic activities such as land use and land cover changes. Urbanization has led to an increase in impervious surfaces, which alters hydrological flow pattern and delivers non-point pollutants to the stream more efficiently. In addition, intensification of agricultural activities can result in the increased nutrient loads due to alternations in surface soil properties. Hence, it is necessary to understand the impact of surrounding environment with specific emphasis on geographical factors (e.g. climate change, land use patterns and landscape metrics) on the RWQ in order to develop sustainable water quality management strategies effectively. We collected pollutant concentration Biochemical Oxygen Demand (BOD), Total Phosphorus (T-P), and&#160; Total Organic Carbon(TOC) from monitoring stations in the Nakdong River watershed. To utilize field monitoring data, we developed a Machine Learning (ML) models (DNN, XGBoost and Random Forest) to predict RWQ in accordance with different environmental factors. SHapley Additive exPlanations (SHAP) was used to illustrate the significance of land uses and landscape patterns on RWQ in Nakdong River. The results of this study can (1) demonstrate the relationship of water quality variables with land uses and landscape patterns, (2) identify pollution sources and factors that affect Nakdong River, and (3) support catchment managers and stakeholders in evaluating the benefits and risks of water management strategies in priority areas.</p>
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