Shanrui Yang

Urban waterfront areas (UWAs), which are essential natural resources and highly perceived public areas in cities, play a crucial role in improving the quality of the urban environment. While numerous studies have delved into the visual perception of urban environments, little attention has been paid to understanding how the visual perception of urban riverfront streets (URSs) differs with various aspects within their unique spatial environment. This study took the Gusu District in Suzhou, China, as a case study, applying deep learning to street-view images to identify urban riverside landscape elements and evaluate their visual attention, aesthetic preference, and distinctiveness through eye-tracking technology and questionnaires. Subsequently, a multidimensional assessment was conducted to analyze how landscape elements influence visual perception in the urban riverfront street. This study concludes that (1) riverfront streets in the Gusu District present balanced visual attention, with high aesthetic preference but limited distinctiveness, and only a few roads in the ancient city score highly for distinctiveness. (2) Greenery, traditional-style buildings, water, and riverfronts positively impact visual perception, while buildings have a negative impact, and backgrounds such as the sky and roads exhibit minimal influence. This study validated the scientific accuracy, appropriateness, and precision of assessments of visual attention, aesthetics, and distinctiveness to quantitatively evaluate the multidimensional human perception of URSs.

Constructed wetlands for effluent treatment (CW-ET) play a vital role in the degradation of pollutants, the purification of water, and the improvement of freshwater ecosystems. However, conventional designs often lack a methodical approach for quantifying the efficacy of these wetlands. In this context, numerical simulations aid in optimizing vegetation selection and placement in these systems, thereby enhancing their overall efficiency. In this study, the MIKE21 hydrodynamic (HD) module was coupled with the advection–dispersion (AD) module to simulate the Yingtai CW-ET in Hai’an. Accordingly, key parameters involved in effective water purification were calibrated and the system’s performance in treating effluent from wastewater treatment facilities was evaluated. The findings demonstrated significant removal efficiencies for chemical oxygen demand (COD), total nitrogen (TN), total phosphorus (TP), and ammonia (NH₃-N), with average rates of 51.14%, 43.14%, 63.82%, and 54.38%, respectively. In addition, the simulations exhibited high accuracy, with hydrodynamic predictions deviating by under 5% and water quality approximations by under 15%. The treated water quality met the requirements for Class IV surface water standards. Utilizing numerical simulations offers valuable insights for the design and performance evaluation of future constructed wetlands.

Constructed Wetlands for Effluent treatment (E-CW) play a vital role in the degradation of pollutants, purification of water, and the improvement of freshwater ecosystems. However, conventional designs often lack a methodical approach for quantifying the efficacy of these wetlands, as well as provide feedback on their operational efficiency. In this context, the use of hydrodynamic and water quality simulations facilitates the selection and arrangement of vegetation within these systems, thereby improving their efficiency. The present study utilized the MIKE21 Hydrodynamic (HD) module in conjunction with the ECO-Lab Water Quality (AD) module to perform a numerical simulation of the Yingtai Constructed Wetland for Effluent in Hai'an City. Accordingly, key parameters involved in effective water purification were calibrated and the system's ability to treat effluents from wastewater treatment facilities was assessed. The findings demonstrated significant removal efficiencies for Chemical Oxygen Demand (COD), Total Nitrogen (TN), Total Phosphorus (TP), and ammonia (NH₃-N), with average rates of 51.14%, 43.14%, 63.82%, and 54.38%, respectively. In addition, the simulations exhibited a high degree of accuracy, with hydrodynamic predictions deviating by less than 5% and water quality approximations by less than 15%. The quality of the treated water successfully met the standards set for Class IV surface water, in accordance with the relevant environmental regulations. Additionally, the constructed wetland system satisfactorily met the requirements for effluent purification. As observed, the use of numerical simulations can provide valuable guidelines for the future design and functional assessment of wetlands by offering crucial insights that aid in the optimization of purification processes and vegetation selection.