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The textile industry is one of the most intensive industries in chemical products whose wastewater contains hazardous dyes, pigments, dissolved/suspended solids, and heavy metals. Hence, it is important to effectively treat the wastewater generated by this industry before releasing it into the environment. Although the field of textile wastewater treatment has made tremendous progress, the developed treatment methods should be improved further to make them economically viable and friendly. In this review, characteristics of textile wastewater are introduced. We have primarily focused on the environmental impact and toxicity of textile wastewater. Toxic and harmful contaminants must be removed from textile wastewater to reduce the extent of environmental pollution caused when textile wastewater is released into rivers or reused. Hence, various techniques that are used to treat textile wastewater are discussed. Finally, the challenges faced, and prospects of the methods have been discussed in detail.

Global greenhouse gas (GHG) emissions from the construction industry continue to increase at an annual rate of 1.5%. It is particularly important to understand the characteristics of the building life cycle to reduce its environmental impact. This paper aims to assess the environmental impact of prefabricated buildings and traditional cast-in-situ buildings over the building life cycle using a hybrid model. A case study of a building with a 40% assembly rate in Japan was employed for evaluation. It concluded that the total energy consumption, and carbon emissions of the prefabricated building was 7.54%, and 7.17%, respectively, less than that of the traditional cast-in-situ building throughout the whole life cycle. The carbon emissions reduction in the operation phase reached a peak of 4.05 kg CO2/year∙m2. The prefabricated building was found to cost less than the traditional cast-in-situ building, reducing the price per square meter by 10.62%. The prefabricated building has advantages in terms of reducing global warming, acid rain, and health damage by 15% reduction. With the addition of the assembly rate, the carbon emissions and cost dropped, bottoming out when the assembly rate was 60%. After that, an upward trend was shown with the assembly rate increasing. Additionally, this study outlined that the prefabricated pile foundations is not applicable due to its high construction cost and environmental impact.

To achieve rural revitalization and enhance the development of rural tourism, this study employs a back propagation neural network (BPNN) to construct a rural revitalization development model. Additionally, the Grey Relation Analysis (GRA) algorithm is used to classify rural revitalization efforts across different cities. Consistency testing is applied to analyze rural revitalization indicators, and a tourism service evaluation model is established to assess rural revitalization tourism services from the perspective of smart cities. The research results indicate that: (1) the training results and expected values of the ten cities are relatively consistent, and the classification of rural revitalization development is good; (2) The five major indicators of tourism information services, tourism security services, tourism transportation services, tourism environment services, and tourism management services all meet the consistency test, and the consistency test results are all less than 0.1, confirming the reliability and effectiveness of the research data; (3) The tourism information and management services are mainly evaluated at level C, accounting for 62% and 62.5% respectively. The tourism transportation and safety services are mainly evaluated at level D, and the model can indicate the level of rural revitalization tourism service; (4) Compared with other algorithms, the GRA-BPNN algorithm performs the best in rural revitalization evaluation, with an accuracy of 92.3%, precision of 91.8%, recall rate of 93.7%, and F1 score of 92.7%. This study optimizes the rural revitalization tourism service platform, enhances the quality of rural tourism, promotes the development of the rural tourism industry, and contributes to the realization of rural revitalization.

Achieving a “win–win” situation regarding economic growth and environmental protection has become a common goal for sustainable development in all countries around the world. As the world’s largest developing country and the second largest economy, China has been striving to maintain economic growth while improving environmental quality to achieve its sustainable development goals. Applying the decoupling approach, a model widely used to quantify the relationship between the environment and the economy, this study analyzed the relationship between the economy and the environment, examining the decoupling performance of economic growth and environmental impacts in 30 Chinese provinces, autonomous regions, and municipalities to investigate whether economic growth and environmental protection have achieved a “win–win” situation. Nighttime light (NTL) data were used to measure the performance of economic growth. In addition, an environmental pressure index (EPI) assessment framework covering 6 primary and 11 secondary indicators was constructed to measure the environmental quality of China over time. First, NTL data proved to be a valid data source for assessing decoupling performance; second, environmental pressure at both the national and provincial levels significantly decreased during the study period; third, the relationship between the economy and the environment has been further improved, and economic growth and environmental protection have achieved a “win–win” situation. These findings offer an in-depth analysis of the decoupling of the economy and the environment in China and serve as a guide for future implementation strategies for sustainable development in various regions.

With the rapid development of low-carbon buildings and energy-saving technologies, phase change materials (PCM) have been extensively applied in building floor heating systems due to their excellent thermal energy storage and temperature control characteristics. However, traditional encased PCM radiant floor heating systems (RFHS) exhibit drawbacks including slow thermal response, low thermal efficiency, and excessive structural layer thickness. This study proposes an innovative solution through CFD numerical simulation: replacing the conventional circular encasement with an elliptical encasement and optimizing the elliptical axis ratio to enhance the system’s thermal performance and reduce mortar layer thickness, thereby lowering construction costs. The study analyzes the thermal state of PCMs, temperature variations of the floor structure, and heat flux distribution. The results show that as the short axis (a) length of the elliptical encasement decreases, the floor’s thermal response becomes faster. When a = 1.5 times the radius of the hot water pipe, the floor achieves the optimal balance between thermal comfort and energy efficiency. Compared to the traditional design, this approach not only enhances thermal response speed and reduces energy consumption but also decreases the mortar layer thickness by 16.7%. This study provides a theoretical basis for optimizing RFHS integrated with PCMs and holds significant potential for engineering applications.

Rapid urbanization increases psychological stress among pedestrians, potentially heightening mental health disorders. This study examines the role of street walls' visual and textural characteristics in stress recovery, using Qingdao as a case study. Virtual reality is employed to simulate five distinct street walls: yellow mortar, brown stone, red brick, green plant, and white mortar. The stress recovery effectiveness of these walls was evaluated through psychological and physiological indicators from 48 young college students. Results indicated that street walls with warm tones, particularly brown stone, significantly aid stress recovery. Psychologically, Restorative Components Scale was highest for brown stone at 1.13. Physiologically, it was linked with notable reductions in diastolic and pulse pressure (decreases of 2.95 mmHg and 2.27 mmHg, respectively), and enhanced parasympathetic activity, as evidenced by the fastest decrease in low frequency/high frequency ratio (LF/HF), and increases in pNN50 and RR (0.14–2.01% and 1.57–11.81 ms, respectively). For urban design, the incorporation of warm-toned materials and natural elements like stone is recommended for their superior restorative benefits.

The proliferation of case studies of shrinking cities in recent years has stimulated intense debate on the impacts of urban shrinkage. However, assessing the impacts of urban shrinkage from a comprehensive perspective could be more present. Also, there is a lake of analytical review of historical studies about the impacts of urban shrinkage. The built environment has different characteristics under different urban development patterns involving infrastructure, services, and social, economic, and structural factors, which provides a best practice for exploring the impacts of urban shrinkage. This study synthesizes the literature surrounding urban shrinkage and built environment changes, identifying that urban shrinkage notably affects the different components of the built environment and gives rise to four related environmental and sustainability impacts involving urban landscapes and structures, ecological sustainability, socioeconomic vitality, and residents’ perceptions. Furthermore, there are interactions between the environmental and sustainability impacts, involving trade-offs and synergies between residents’ perceptions, ecological sustainability, and socioeconomic vitality. The study also summarized the mainstream methods for assessing the impacts of urban shrinkage and explored the effects of urban shrinkage management strategies on improving the built environment. Finally, a framework for future direction is presented for the final to integrate the theories of urban shrinkage, people and land relationship, and sustainable urban development to guide further exploration in the field. In summary, this study implies that restoring and upgrading the built environment can pave the way for a common goal for long-term sustainable development. The value of this study is to provide relevant researchers with the knowledge to understand the developing frontiers of urban shrinkage impacts on built environments.

Balancing economic growth with environmental protection is vital for the sustainable development of cities and regions. However, urban greening has rarely been considered in extensive studies. This study incorporates urban greening into a coupling coordination degree (CCD) model, in order to evaluate its coordination with economic performance. A total of 286 cities in China between 2005 and 2019 were selected as specific study subjects. Meanwhile, clustering method was used to classify different clusters based on CCD values, the Gini coefficient analysis was applied to discover the CCD values inequality characteristics and the exploratory spatial data analysis (ESDA) method was employed to study the CCD values spatial aggregation features. The results indicate that the CCD values presented significant spatial heterogeneity. Spatially, the CCD values were divided into eight clusters, with those in the eastern region generally being higher than in the central and western regions. Temporally, the CCD in all cities showed an increasing trend, but more than 60% of cities were still in the uncoordinated or low-level coordination stage. In addition, inequality and spatial aggregation characteristics were observed in CCD values, both of which presented decreasing trends. Greening has a stronger influence on the linked and coordinated growth of the two systems; therefore, we propose policy recommendations for pursuing the development of environmentally friendly cities from different aspects. In summary, this research allows for a better understanding of economic and environmental relationships, thus contributing to the objective of creating sustainable cities and communities.

The term water security (WS) shows an overwhelming superiority in both policy and academic circles. Firstly, the paper reviews the multiple interpretations of WS. Specifically, we emphasize that urban water security (UWS), as one of the human sustainable development goal, consists of three pillars: water resources security focusing on water scarcity issue; water environment security focusing on water pollution issue, and water disasters security focusing on water-related hazards and vulnerability issue. Secondly, it compares and contrasts the development backgrounds and properties of three water management approaches to UWS--integrated water management, adaptive water management, and intelligence-based water management (skill and equipment intensive). Thirdly, it mainly discusses the development situation of smart water grid and lessons learned from intelligent (smart) water management. Finally, this paper presents the achievements and challenges of the water management approaches. Although every water management approach has its own advantages and disadvantages, our comparison can provide a map for water policy makers and stockholder companies to guide the future development of water management and realizing UWS.

Remote sensing technology plays an increasingly important role in land surface temperature (LST) research. However, various remote sensing data have spatial–temporal scales contradictions. In order to address this problem in LST research, the current study downscaled LST based on three different models (multiple linear regression (MLR), thermal sharpen (TsHARP) and random forest (RF)) from 1 km to 100 m to analyze surface urban heat island (SUHI) in daytime (10:30 a.m.) and nighttime (10:30 p.m.) of four seasons, based on Moderate Resolution Imaging Spectroradiometer (MODIS)/LST products and Landsat 8 Operational Land Imager (OLI). This research used an area (25 × 25 km) of Hangzhou with high spatial heterogeneity as the study area. R2 and RMSE were introduced to evaluate the conversion accuracy. Finally, we compared with similarly retrieved LST to verify the feasibility of the method. The results indicated the following. (1) The RF model was the most suitable to downscale MODIS/LST. The MLR model and the TsHARP model were not applicable for downscaling studies in highly heterogeneous regions. (2) From the time dimension, the prediction precision in summer and winter was clearly higher than that in spring and autumn, and that at night was generally higher than during the day. (3) The SUHI range at night was smaller than that during the day, and was mainly concentrated in the urban center. The SUHI of the research region was strongest in autumn and weakest in winter. (4) The validation results of the error distribution histogram indicated that the MODIS/LST downscaling method based on the RF model is feasible in highly heterogeneous regions.