Engineering ecology for digital water pollution control: A collaborative innovation framework

Water pollution governance is increasingly challenged by fragmented institutions, disconnected data systems, and delayed responses to cross-regional environmental risks. This study develops a Global Information Modelling (GIM)-based engineering ecology framework for digital water pollution control. The framework combines three elements: interoperable environmental data streams, digital twins and artificial intelligence for real-time analysis, and collaborative governance mechanisms that connect regulators, utilities, researchers, enterprises, and affected communities. Unlike conventional governance models that treat monitoring, decision-making, and implementation as separate stages, the proposed framework links them within an adaptive feedback loop. To clarify operational feasibility, the study adds a pilot scenario for a transboundary river basin, showing how GIM can integrate upstream and downstream pollution data, simulate pollutant transport, and support coordinated response. The paper also contrasts the proposed framework with existing command-and-control, market-based, and collaborative governance approaches. Potential barriers are discussed, including data privacy, uneven digital infrastructure, data quality, institutional resistance, and financing constraints. The framework contributes to ecological governance by translating digital technologies into an accountable, interoperable, and learning-oriented system for water pollution management.