Assessing carbon neutrality potential of constructed wetlands: an improved neural network-based strategy for environmental impact analysis and control

Constructed wetlands (CWs) effectively complement conventional wastewater treatment systems while their carbon neutrality potential remains uncertain due to empirical estimation limitations. This study develops an innovative deep neural network (DNN)-based framework that significantly improves environmental impact assessment accuracy for CWs, particularly for carbon neutrality potential and pollutant flux evaluation. The enhanced DNN model demonstrates superior predictive performance (R² > 0.9) for treatment efficiency and greenhouse gas emissions across diverse operational scenarios, overcoming traditional empirical approach limitations. Results indicate properly managed CW systems function as net carbon sinks, with 30-year operational lifespans offsetting construction-phase emissions while facilitating additional carbon sequestration through vegetation-mediated processes, though marine ecotoxicity and abiotic depletion remain key environmental impacts. Two primary optimization strategies were identified: strategic utilization of low-carbon construction materials and reduced fossil fuel dependency. The strategies synergistically enhance carbon sequestration while minimizing secondary environmental impacts. As the integrated framework combining advanced machine learning with life cycle assessment, this work provides a scientifically grounded approach for sustainable CW design and operation, offering policymakers data-driven solutions for achieving carbon-neutral wastewater treatment objectives.