Dispositional mindfulness and fatigue in Chinese nurses during the COVID-19 pandemic: a possible mediating role of sleep quality (preprint)

Purpose: During the COVID-19 epidemicin China, clinical nurses are at an elevated risk of suffering fatigue. This research sought to investigate the correlation between dispositional mindfulness and fatigue among nurses, as well as the potential mediation role of sleep quality in this relationship. Methods: This online cross-sectional survey of nurses was performed from August to September 2022 after the re-emergence of COVID-19 in China. The Mindful Attention Awareness Scale (MAAS), 14-item Fatigue Scale (FS-14), and Pittsburgh Sleep Quality Index (PSQI) were employed to assess the levels of dispositional mindfulness, fatigue, and sleep quality, respectively. The significance of the mediation effect was determined through a bootstrap approach with SPSS PROCESS macro. Results: A total of 2143 nurses completed the survey. Higher levels of dispositional mindfulness were significantly negatively related to fatigue (r = -0.518, P < 0.001) and sleep disturbance (r = -0.344, P < 0.001). Besides, there was a positive relationship between insufficient sleep and fatigue (r = 0.547, P < 0.001). Analyses of mediation revealed that sleep quality partly mediated the correlation between dispositional mindfulness and fatigue (β= -0.551, 95% Confidence Interval = [-0.630, -0.474]). Conclusions: Chinese nurses' dispositional awareness was related to the reduction of fatigue during the COVID-19 pandemic, and this relationship indirectly operates through sleep quality. Intervention strategies and measures should be adapted to improve dispositional mindfulness and sleep quality to reduce fatigue in nurses during the pandemic.

Global Gridded Daily CO2 Emissions (preprint)

Precise and high-resolution carbon dioxide (CO2) emission data is of great importance of achieving the carbon neutrality around the world. Here we present for the first time the near-real-time Global Gridded Daily CO2 Emission Datasets (called GRACED) from fossil fuel and cement production with a global spatial-resolution of 0.1{\deg} by 0.1{\deg} and a temporal-resolution of 1-day. Gridded fossil emissions are computed for different sectors based on the daily national CO2 emissions from near real time dataset (Carbon Monitor), the spatial patterns of point source emission dataset Global Carbon Grid (GID), Emission Database for Global Atmospheric Research (EDGAR) and spatiotemporal patters of satellite nitrogen dioxide (NO2) retrievals. Our study on the global CO2 emissions responds to the growing and urgent need for high-quality, fine-grained near-real-time CO2 emissions estimates to support global emissions monitoring across various spatial scales. We show the spatial patterns of emission changes for power, industry, residential consumption, ground transportation, domestic and international aviation, and international shipping sectors between 2019 and 2020. This help us to give insights on the relative contributions of various sectors and provides a fast and fine-grained overview of where and when fossil CO2 emissions have decreased and rebounded in response to emergencies (e.g. COVID-19) and other disturbances of human activities than any previously published dataset. As the world recovers from the pandemic and decarbonizes its energy systems, regular updates of this dataset will allow policymakers to more closely monitor the effectiveness of climate and energy policies and quickly adapt