Bridging Computational Biophysics and Citizen Science: In Silico Toxicity Profiling of Industrial Pollutants in Coastal Flora

Coastal ecosystems face unprecedented degradation due to industrial runoff, necessitating proactive, scalable ecotoxicology models. The traditional live-exposure testing is logistically restricted and often ethically fraught, typically measuring damage only after population decline is already underway in the wild. This study introduces a dual-pronged framework that integrates molecular biophysics with community-driven environmental monitoring to predict and mitigate the impacts of aquatic pollutants on the foundational marine flora. In the computational phase, molecular docking and molecular dynamics simulations are employed to model the interactions between prevalent industrial toxins, such as persistent agricultural pesticides and heavy metals, and critical botanical targets. Specifically, this research examines structural disruptions to RuBisCO and Photosystem II in indicator species such as Zostera marina. By calculating the binding affinity (ΔG) and mapping conformational changes over time, the in silico approach provides rapid, predictive data on photosynthetic inhibition before systemic ecological failure occurs. Concurrently, the applied field phase establishes a community-integrated coastal pollution monitoring system. By equipping local stakeholders and environmental advocacy networks with standardized water-quality testing protocols, a high-density, real-time spatial mapping of runoff zones is achieved. This creates a critical scientific feedback loop: grassroots monitoring identifies the most immediate chemical threats accumulating in specific coastal sectors, which are subsequently analyzed through the in silico pipeline to determine their precise molecular toxicity to native vegetation. Ultimately, this methodology bridges the theoretical biophysics and on-the-ground conservation. By translating the complex protein-ligand disruption data into tangible ecological risk assessments, the framework provides the robust scientific evidence required to inform local environmental policy, enforce industrial regulations, and empower community-led habitat protection.