Introduction to the Special Issue "Super High Arch Dams and Underground Caverns in China"

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A Real-Time Location-Based Services System Using WiFi Fingerprinting Algorithm for Safety Risk Assessment of Workers in Tunnels

This paper investigates the feasibility of a real-time tunnel location-based services (LBS) system to provide workers’ safety protection and various services in concrete dam site. In this study, received signal strength- (RSS-) based location using fingerprinting algorithm and artificial neural network (ANN) risk assessment is employed for position analysis. This tunnel LBS system achieves an online, real-time, intelligent tracking identification feature, and the on-site running system has many functions such as worker emergency call, track history, and location query. Based on ANN with a strong nonlinear mapping, and large-scale parallel processing capabilities, proposed LBS system is effective to evaluate the risk management on worker safety. The field implementation shows that the proposed location algorithm is reliable and accurate (3 to 5 meters) enough for providing real-time positioning service. The proposed LBS system is demonstrated and firstly applied to the second largest hydropower project in the world, to track workers on tunnel site and assure their safety. The results show that the system is simple and easily deployed

Assessing the recent impact of COVID-19 on carbon emissions from China using domestic economic data

The outbreak of coronavirus disease 2019 (COVID-19) has caused tremendous loss to human life and economic decline in China and worldwide. It has significantly reduced gross domestic product (GDP), power generation, industrial activity and transport volume; thus, it has reduced fossil-related and cement-induced carbon dioxide (CO 2) emissions in China. Due to time delays in obtaining activity data, traditional emissions inventories generally involve a 2–3-year lag. However, a timely assessment of COVID-19's impact on provincial CO 2 emission reductions is crucial for accurately understanding the reduction and its implications for mitigation measures; furthermore, this information can provide constraints for modeling studies. Here, we used national and provincial GDP data and the China Emission Accounts and Datasets (CEADs) inventory to estimate the emission reductions in the first quarter (Q1) of 2020. We find a reduction of 257.7 Mt. CO 2 (11.0%) over Q1 2019. The secondary industry contributed 186.8 Mt. CO 2 (72.5%) to the total reduction, largely due to lower coal consumption and cement production. At the provincial level, Hubei contributed the most to the reductions (40.6 Mt) due to a notable decrease of 48.2% in the secondary industry. Moreover, transportation significantly contributed (65.1 Mt), with a change of −22.3% in freight transport and −59.1% in passenger transport compared with Q1 2019. We used a point, line and area sources (PLAS) method to test the GDP method, producing a close estimate (reduction of 10.6%). One policy implication is a change in people's working style and communication methods, realized by working from home and holding teleconferences, to reduce traffic emissions. Moreover, GDP is found to have potential merit in estimating emission changes when detailed energy activity data are unavailable. We provide provincial data that can serve as spatial disaggregation constraints for modeling studies and further support for both the carbon cycle community and policy makers