Pervious concrete is recommended, which is of great benefit to the ecological environment and human living environment. In this paper, the influences of five water-cement ratios and four fly ash contents to replace the cement by mass with a water-cement ratio of 0.30 on the properties of Recycled Aggregate Pervious Concrete (RAPC) were studied. Following this, based on the Grey relational-Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) optimization method, the strength, permeability, abrasion loss rate, and material costs of RAPC were adopted as evaluation indices to establish a mix proportion optimization model. The results show that the increase of water-cement ratio and fly ash replacement level of RAPC leads to decreased compressive strength while an increase in the permeability and abrasion loss rate. According to test results based on the optimal model 0.30 was identified as the best mix proportion. In addition, ecological-economic analysis of RAPC raw materials was carried out by comparing different natural aggregates. The results of EE (embodied energy) and ECO2e (embodied CO2 emission) pointed out that the combination of recycled aggregate and fly ash leads to significant ecological and economic benefits.

The ecological health level of water resources plays a crucial role in achieving sustainable development in society. This study focuses on the core area of the Central Plains urban agglomeration. A comprehensive evaluation system, based on the Water Resources - Socio-Economic - Ecological Environment model framework, was constructed, incorporating 22 evaluation indicators in the indicator layer. Using an improved entropy weighting method combined with the ITOPSIS model, the water ecological health level in the study area from 2013 to 2022 was evaluated, and corresponding measures were proposed based on the evaluation results. Furthermore, the Barrier Degree model was applied to identify the eight major hindering factors influencing the water resource ecological environment in the research area. The results show that: The overall water resource ecological health level in the core area of the Central Plains urban agglomeration from 2013 to 2022 showed a generally improving trend. The eight major hindering factors indicate that cities should focus on green urban development while promoting the construction of smart water management systems and sponge cities.

The aim of this article is to reduce the environmental harm caused by industrial solid waste, specifically tannery sludge, and enable its reutilization. This study prepared an alkali-activated tannery sludge–slag solidification product (ATSSP) with high mechanical properties using blast furnace slag and tannery sludge as raw materials. The response surface methodology (RSM) was used to optimize the product mix ratio. The hydration mechanism and solidification method of ATSSP for Cr in tannery sludge were studied using X-ray diffraction (XRD), scanning electron microscope-energy dispersive spectrometer (SEM-EDS), and Fourier transform infrared reflection (FT-IR). The results indicate that the compressive strength regression model established through RSM has high accuracy and credibility. When the ratio of activator to binder is 0.2174, the alkali activation modulus is 1.02, and the water-to-cement ratio is 0.37; the 28 d compressive strength of ATSSP can reach 71.3 MPa. The sulfate in tannery sludge can promote the secondary hydration reaction of slag, and the generated hydrated calcium silicate and calcite greatly improve the strength of the ATSSP. The reducing substances contained in slag can reduce Cr (VI) in tannery sludge to Cr (III) in the form of uvarovite. The total Cr and Cr (VI) precipitation concentrations of the product are far less than the specification requirements.

The acceleration of urbanization and climate change has increasingly impacted the health level of urban dual water cycles. In order to accurately evaluate the health status of urban water cycles, the evaluation system covers four standard layers of water ecology, water abundance, water quality and water use, including 19 basic indicators such as water storage change and annual average precipitation. Three-scale AHP and EFAST algorithms are adopted to set the criterion and index layer weights. Water-cycle health assessment models are based on the improved TOPSIS model. The model evaluated Zhengzhou’s water cycle health from 2011 to 2021. We compared the TOPSIS model and FCE method to ensure the scientific objectivity of the evaluation results. The evaluation results indicated that the water cycle in Zhengzhou City improved annually, and the relative progress in 2020 was 0.567 in a sub-health state. The eco-environmental water demand, green coverage rate of the built district, water resources amount, and industry’s water consumption per unit of value added (CNY 10,000) were the major obstacles. These four factors have preponderantly influenced Zhengzhou City’s water cycle health. Our research results provide scientific reference for Zhengzhou to achieve a healthy urban water cycle and regional sustainable development.

Under the influence of human activities and climate change, the development of the Yellow River basin (YRB) is constrained by the utilization of hydropower resources, carbon emission and ecological deterioration. In this paper, combined weighting method, comprehensive assessment index, coupled coordination degree and obstacle degree model were used to construct a theoretical framework of the water-energy-carbon-ecological environment (WECEc) system in the nine provinces of the YRB from 2013 to 2022, analyze the comprehensive development level of the system and the spatio-temporal evolution characteristics of the coupled coordination level of the system, and analyze obstacle factors. The results show that (1) During the study period, the comprehensive development level of WECEc system in the YRB generally increased in a fluctuation pattern. Among them, the water resources subsystem showed a fluctuating upward trend, the energy subsystem showed a slow and steady upward trend, the carbon subsystem showed a steady upward trend, and the ecological environment subsystem showed a significant upward trend. (2) The coupling coordination degree of WECEc system in nine provinces and regions of the YRB is concentrated as “IV” level “Primary Coordination” type. The overall coupling coordination degree of WECEc system in the YRB showed a steady increase in time, but in space, it showed a stepped distribution with higher coordination degree in the middle and upper reaches and lower coordination degree in the lower reaches, with significant regional differences. (3) Total water resources (WS1), Per capita carbon storage (CS4), coal production (ER2), water production modulus (WS2), carbon storage of forest and grass vegetation (CS1), total energy production (ER1), carbon sink of forest and grass vegetation (CS2) and domestic water consumption (WD3) play an important role in the coordinated development of WECEc system in the YRB. They are also the main obstacle factors that affects the coupling coordination degree of the system. This paper provides research basis and policy suggestions for promoting the sustainable development of the coupling system of WECEc in the nine provinces of the YRB, and promoting the high-quality development of Economy-Society-Ecology (ESE) in the YRB.

Seepage analysis is always a concern in dam safety and stability research. The prediction and analysis of seepage pressure monitoring data is an effective way to ensure the safety and stability of dam seepage. With the timeliness of a change in a monitoring value and lag due to external influences, a RS‐LSTM model written in Python is developed in this paper which combines rough set theory (RS) and the long‐ and short‐term memory network model (LSTM). The model proposed calculates the prediction score of the seepage pressure of a dam experiencing multiple effects by preordering factor importance values to eliminate the interference of redundant factors. A case study shows that the water level, rainfall, temperature, and duration are all factors that affect the seepage pressure, and their importance values decrease successively. Thus, the seepage pressure of a dam can be predicted with a determination coefficient R² of 0.96. Compared with the recurrent neural network (RNN) model and BP neural network model, the training time of the RS‐LSTM model proposed is 6.37 s, and the operation efficiency is 41% and 59% higher than that of the RNN and BP models, respectively. The mean relative error is also 3.00%, which is 50% lower than that of the RNN model and 31% lower than that of the BP model. Based on these results, this model has the advantages of fast computation speed and high accuracy in prediction.