Data on point cloud scanning and ground radar of composite lining in jointly constructed tunnel.

The present dataset pertains to field records of construction quality of composite lining in a jointly constructed tunnel. The dataset includes the original mining surface profile data collected by the terrestrial laser scanning (TLS) and radar information on backfill quality outside the segmental lining which was obtained by the ground-penetration radar (GPR) detection. The point cloud data of the mining surface was further processed and compared with the design tunnel model to evaluate the level of over and under- excavation. The radargram provides details on the variation of the signal waveform by which the heterogeneity of backfill can be recognized. The dataset can be used to verify that the voids are prone to occur in the outside backfill of the composite lining. Furthermore, this dataset provides a method for detecting and preventing the defects of the composite lining and also facilitates the post-construction treatment. Additional foreseeable use of this dataset includes providing modeling material for researchers interested in knowing how voids in backfill influence the behavior of composite lining. As a supplement, this dataset supports the numerical analysis outlined in the article titled "Numerical evaluation of segmental tunnel lining with voids in outside backfill" [1].

Approach based on TOPSIS and Monte Carlo simulation methods to evaluate lake eutrophication levels.

This study presents an approach for eutrophication evaluation based on the technique for order preference by similarity to an ideal solution (TOPSIS) method and Monte Carlo simulation (MCS). The MCS is employed to produce a normally distributed dataset based on the observed data while the TOPSIS method and membership function are used to evaluate the level of eutrophication. Herein, a eutrophication problem in Lake Erhai is evaluated to check the performance of the proposed approach. The evaluation results were consistent with the real situation when the coefficient P in the membership function is equal to 1. Moreover, the developed approach is able to (i) deal with evaluation items with inherent fuzziness and uncertainties, (ii) improve the reliability of evaluation results via MCS, and (iii) raise the tolerance to errors in measured data. A global sensitivity analysis indicated that the potassium permanganate index (CODMn) and Secchi disc (SD) are the most sensitive factors in the developed approach. Finally, a range for the coefficient P value in the membership function was recommended.

Land Subsidence Control Zone and Policy for the Environmental Protection of Shanghai.

Land subsidence was once a major geo-hazard in the city of Shanghai, China. From 1921 to 1965, the maximum cumulative land subsidence in the urban areas of China reached 2.6 m. This large subsidence has resulted in high economic losses for Shanghai. The Regulation of Prevention and Control of Land Subsidence of Shanghai Municipality was published in 2013 (simply cited as the 2013-regulation in the following context). The characteristics of the 2013-regulation included the combination of the subsidence monitoring network and the groundwater detection network due to both the effects of groundwater withdrawal and construction. In addition, the setting up of a supervision system was also incorporated in the 2013-regulation. To control the land subsidence, Shanghai demarcated three land subsidence control zones, where special measures have been implemented. From a strategic environmental assessment (SEA) point of view, the 2013-regulation attains a high total score, indicating that the control of groundwater withdrawal and recharge is effective. The observed land subsidence over the past six years also confirms the effectiveness of the 2013-regulation with the most consideration of SEA for sustainable environment protection in Shanghai. However, more effort should be made in the implementation of SEA in land subsidence control in the future.

New Policy and Implementation of Municipal Solid Waste Classification in Shanghai, China.

To promote sustainable development, the Chinese government launched a new municipal solid waste (MSW) classification strategy in 2017. Shanghai was selected as one of the first pilot cities for MSW classification. The Shanghai municipal government first established the new MSW classification policy in 2017. The Shanghai Municipal Solid Waste Management Regulation was published in 2019 and came into effect on 1 July 2019. This short communication reports on Shanghai's new MSW classification policy and its implementation. The main content and measures adopted by Shanghai's government to ensure the effective implementation of the new MSW classification policy are introduced. Besides, a SWOT (i.e., strengths, weaknesses, opportunities, and threats) analysis on the present policy and measures is conducted, and based on the results, some discussions and suggestions regarding the implementation of MSW classification in Shanghai and the whole of China are presented.