فهرست نشریات علمی دسترسی آزاد ایران

The governance of water quality and aquatic ecosystems remains a critical challenge in environmental law and policy. This study explores how predictive modeling can complement legal frameworks to improve water quality interventions. By collecting comprehensive data—including pH, dissolved oxygen (DO), biochemical oxygen demand (BOD), nitrate, and turbidity—from various U.S. watersheds, baseline conditions and significant seasonal and spatial variability were identified. Using multivariate regression and Lasso modeling, BOD levels were predicted, with model validity confirmed through cross-validation. Optimization methods were then applied to develop strategies based on enforceable legal thresholds. All key indicators showed significant improvements, highlighting the effectiveness of data-informed thresholds in guiding compliance. Seasonal changes notably influenced water quality; for instance, pH levels consistently dropped during rainy seasons, with the Rio Grande showing a decline from 6.8 to 6.5 due to acidic runoff. DO levels also decreased during wet periods, likely because of lower oxygen solubility and increased organic matter decomposition. These findings support dynamic, multi-level regulation that integrates scientific data with legal standards. This integrated approach is crucial for addressing persistent and complex environmental issues, ensuring that policy and regulation are both scientifically grounded and practically enforceable.