Background As COVID-19 continues to spread globally, traditional emergency management measures are facing many practical limitations. The application of big data analysis technology provides an opportunity for local governments to conduct the COVID-19 epidemic emergency management more scientifically. The present study, based on emergency management lifecycle theory, includes a comprehensive analysis of the application framework of China’s SARS epidemic emergency management lacked the support of big data technology in 2003. In contrast, this study first proposes a more agile and efficient application framework, supported by big data technology, for the COVID-19 epidemic emergency management and then analyses the differences between the two frameworks. Methods This study takes Hainan Province, China as its case study by using a file content analysis and semistructured interviews to systematically comprehend the strategy and mechanism of Hainan’s application of big data technology in its COVID-19 epidemic emergency management. Results Hainan Province adopted big data technology during the four stages, i.e., migration, preparedness, response, and recovery, of its COVID-19 epidemic emergency management. Hainan Province developed advanced big data management mechanisms and technologies for practical epidemic emergency management, thereby verifying the feasibility and value of the big data technology application framework we propose. Conclusions This study provides empirical evidence for certain aspects of the theory, mechanism, and technology for local governments in different countries and regions to apply, in a precise, agile, and evidence-based manner, big data technology in their formulations of comprehensive COVID-19 epidemic emergency management strategies.
Kang Liu,Mengmeng Wang,Qiaozhi Zhang,Shanta Dutta,Tianle Zheng,Marjorie Valix,Daniel C.W. Tsang
Copper plays a crucial role in the low-carbon transformation of global communities with prevalent use of electric vehicles. This study proposed an environmentally friendly approach that utilizes a deep eutectic solvent (DES), choline chloride-ethylene glycol (ChCl-EG), as green solvent for the selective extraction of copper from scrap materials . With hydrogen peroxide as an oxidizing agent , the copper species from the printed circuit boards (PCBs) scraps were efficiently leached by the DES through oxidation-complexation reactions (conditions: 25 min, 20 °C, and 5 wt% H 2 O 2 ). Molecular dynamics and density functional theory were performed to simulate the intricate cascade of interactions between copper species and hydrogen bond donors/acceptors of DES, providing insights into the mechanistic processes involved. Copper was selectively recovered from the DES leachate containing impurities (e.g. , Pb 2+ , Sn 2+ , and Al 3+ ) through electrodeposition via a diffusion-controlled reaction under a constant potential mode. A comprehensive life cycle assessment of the process demonstrated that the utilisation of DES in the extraction of copper from waste PCBs could result in significant reduction in carbon dioxide emissions (−93.6 kg CO 2 eq of 1000 kg waste PCBs), thus mitigating the carbon footprint of global copper use through the proposed solvometallurgical recycling process of secondary resources.
危险品化学品经营许可证（带存储）