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The study applied a tiered ecological risk assessment method to evaluate the long-term status and trend of the ecological risks of dissolved heavy metals from 2011 to 2019 in the Yangtze River Estuary and Zhejiang coastal waters, China. The results for spring, summer, and autumn of 2019 indicated that Pb, Cd, and Zn posed no adverse ecological risk, Cu posed a potential ecological risk, and As posed an ecological risk. The annual results from 2011 to 2019 suggested that Pb, Cd, and Zn posed no adverse ecological risks, and As and Cu posed an ecological risk. The trend analysis in the nine years showed that the ecological risk of Cu is gradually decreasing, while that of As is still a concern. The overall trend is attributed to the environmental protection policies that reduced these contaminants' terrestrial sources and atmospheric sources. • A tiered method can be used to evaluate the ecological risks of dissolved heavy metals in the region. • The annual results from 2011 to 2019 suggested that As and Cu posed ecological risks. • The nine-year trend analysis suggested both As and Cu should be monitored and take steps to reduce the source inputs. • The environmental protection policies efficiently reduced terrestrial and atmospheric sources of these contaminants. [ABSTRACT FROM AUTHOR]

• Novel GRU-LIME prediction method boosts accuracy. • GRU consistently beats traditional models for reliable predictions. • Renewable energy insights inform sustainable urban development. This study proposes a novel approach to sustainable energy transition in urban environments, focusing on the prediction and management of renewable energy outputs for low-carbon urban development. We introduce a new intelligent prediction model based on Gated Recurrent Unit (GRU) Networks to forecast the output power of both tidal and biomass units. The GRU model, known for its capability in capturing sequential dependencies, is employed to enhance the accuracy of renewable energy predictions in the context of urban sustainability. To address the interpretability challenges inherent in complex deep learning models, we leverage LIME (Local Interpretable Model-agnostic Explanations). By integrating LIME into our GRU-based prediction model, we enhance transparency and understandability. This approach facilitates the identification of key factors influencing predictions, providing stakeholders and decision-makers with valuable insights for effective sustainable energy management. The proposed hybrid model is applied to the digital model of real tidal and biomass sites in China, allowing for a statistical examination of renewable integration and energy management strategies. The results offer a comprehensive understanding of the dynamics between renewable energy sources and urban development, paving the way for informed decision-making in the pursuit of low-carbon and resilient cities. This research contributes to the broader discourse on sustainable urban development by offering a robust statistical framework for renewable energy integration and management in urban environments. [ABSTRACT FROM AUTHOR]