Modeling Stream Network ecology and Industrial symbiosis to mitigate the impacts of Pharmaceutical manufacturing on the freshwater eco-systems

The increasing fragmentation of freshwater ecosystems due to anthropogenic activities, such as dam construction and industrial pollution, has raised significant concerns for bio- diversity conservation and sustainable water resource management. This thesis develops a novel network connectivity-based stream classification framework to systematically analyze stream networks, assess human-induced disruptions, and propose sustainable industrial prac- tices to mitigate their ecological impact. In the first phase, we develop a graph-theoretic model of stream networks, leveraging network science, statistical learning, and fuzzy logic to classify stream segments based on Network connectivty. We further developed the Net- ConUS dataset, which includes over 3.69 million stream segments across the conterminous United States. The second phase investigates the anthropogenic impact on stream networks, particularly the effects of dams, reservoirs, and infrastructure on freshwater connectivity. We develop a dataset to quantify the amount of fragmentation for the Conterminous United States.

The final phase focuses on the sustainable management of pharmaceutical waste, a major contributor to freshwater pollution. We propose a Multi-Criteria Decision-Making (MCDM) framework, integrating Analytic Hierarchy Process (AHP) and TOPSIS, to systematically evaluate pharmaceutical waste treatment technologies based on environmental impact, treat- ment efficiency, economic feasibility, and scalability. Furthermore, we introduce a chemistry- driven weighting approach, incorporating Lipinskis physicochemical properties (molecular weight, LogP, TPSA, HBA, HBD) to optimize technology selection. Additionally, a Nash Bargaining-based game theory model is employed to ensure fair decision-making between pharmaceutical industries and sustainability experts. By integrating network-based stream classification, machine learning-driven ecological impact assessment, and decision-support models for industrial sustainability, this research presents a comprehensive framework for preserving freshwater ecosystems while optimizing industrial resource management. The findings contribute to advancing ecological modeling, industrial symbiosis, and sustainable pharmaceutical manufacturing, offering valuable insights for policymakers, environmental regulators, and industry stakeholders