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The public's acceptance level of recycled water use is a key factor that affects the popularization of this technology; therefore, it is critical to know the public's attitude in order to make guiding policies effectively and scientifically. To examine the major focuses and hot topics among the public about recycled water use, one of the major platforms for social opinion in China, the micro blog, is used as a source to obtain data related to the topic. Through the "follow-be followed" and "forward-dialogue" behaviors, a network of discussion of recycled water use among micro-blog users has been constructed. Improved particle swarm optimization has been used to allow deep digging for key words. Ultimately, key words about the topic of have been clustered into three categories, namely, the popularization status of recycled water use, the main application, and the public's attitude. The conclusion accurately describes the concerns of Chinese citizens regarding recycled water use, and has important significance for the popularization of this technology.

Urban agglomeration, an established urban spatial pattern, contributes to the spatial association and dependence of city-level CO 2 emission distribution while boosting regional economic growth. Exploring this spatial association and dependence is conducive to the implementation of effective and coordinated policies for regional level CO 2 reduction. This study calculated CO 2 emissions from 2005-2016 in the Chengdu-Chongqing urban agglomeration with the IPAT model, and empirically explored the spatial structure pattern and association effect of CO 2 across the area leveraged by the social network analysis. The findings revealed the following: (1) The spatial structure of CO 2 emission in the area is a complex network pattern, and in the sample period, the CO 2 emission association relations increased steadily and the network stabilization remains strengthened; (2) the centrality of the cities in this area can be categorized into three classes: Chengdu and Chongqing are defined as the first class, the second class covers Deyang, Mianyang, Yibin, and Nanchong, and the third class includes Zigong, Suining, Meishan, and Guangan-the number of cities in this class is on the rise; (3) the network is divided into four subgroups: the area around Chengdu, south Sichuan, northeast Sichuan, and west Chongqing where the spillover effect of CO 2 is greatest; and (4) the higher density of the global network of CO 2 emission considerably reduces regional emission intensity and narrows the differences among regions. Individual networks with higher centrality are also found to have lower emission intensity.

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In order to verify whether environmental education can play guiding role among the individuals in terms of the reuse of recycled water, and to further confirm its mechanism of action, a structural equation model was established in this study regarding to the influence of knowledge about recycled water on acceptability of recycled water. Besides, a survey was made among 714 individuals. The structural equation model revealed that high levels of knowledge about recycled water stimulate individuals' trust in water authorities, and reduce their perceived risk of recycled water. More specially, knowledge about recycled water was reported to be a significant predictor of individuals' acceptability of recycled water. Individuals' high levels knowledge about recycled water can affect their acceptability of recycled water indirectly through two different ways, while high levels knowledge about recycled water can reduce the individuals' perceived risk of recycled water, so it could increase their acceptability of recycled water indirectly. Another influence path is that high levels knowledge about recycled water could reduce individuals' perceived risk of recycled water by improving their trust in the water authorities, and ultimately improve their acceptability of recycled water. In this paper, it has proved that environmental education has significance guiding effect on individuals in terms of the use of recycled water, and provided a clue as how does environmental education affects individuals' behavior to use recycled water.

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Construction industry is the largest data industry, but with the lowest degree of datamation. With the development and maturity of BIM information integration technology, this backward situation will be completely changed. Different business data from a construction phase and operation and a maintenance phase will be collected to add value to the data. As the BIM information integration technology matures, different business data from the design phase to the construction phase are integrated. Because BIM integrates massive, repeated, and unordered feature text data, we first use integrated BIM data as a basis to perform data cleansing and text segmentation on text big data, making the integrated data a “clean and orderly” valuable data. Then, with the aid of word cloud visualization and cluster analysis, the associations between data structures are tapped, and the integrated unstructured data is converted into structured data. Finally, the RNN-LSTM network was used to predict the quality problems of steel bars, formworks, concrete, cast-in-place structures, and masonry in the construction project and to pinpoint the occurrence of quality problems in the implementation of the project. Through the example verification, the algorithm proposed in this paper can effectively reduce the incidence of construction project quality problems, and it has a promotion. And it is of great practical significance to improving quality management of construction projects and provides new ideas and methods for future research on the construction project quality problem.