No evidence for global decrease in CO2 concentration during the first wave of COVID-19 pandemic.

Numerous studies have reported that CO2 emissions have decreased because of global lockdown during the first wave of the COVID-19 pandemic. However, previous estimates of the global CO2 concentration before and after the outbreak of the COVID-19 pandemic are limited because they are based on energy consumption statistics or local specific in-situ observations. The aim of the study was to explore objective evidence for various previous studies that have claimed the global CO2 concentration decreased during the first wave of the COVID-19 pandemic. There are two ways to measure the global CO2 concentration: from the top-down using satellites and the bottom-up using ground stations. We implemented the time-series analysis by comparing the before and after the inflection point (first wave of COVID-19) with the long-term CO2 concentration data obtained from World Meteorological Organization Global Atmosphere Watch (WMO GAW) and Greenhouse Gases Observing Satellite (GOSAT). Measurements from the GOSAT and GAW global monitoring stations show that the CO2 concentrations in Europe, China, and the USA have continuously risen in March and April 2020 compared with the same months in 2019. These data confirm that the global lockdown during the first wave of the COVID-19 pandemic did not change the vertical CO2 profile at the global level from the ground surface to the upper layer of the atmosphere. The results of this study provide an important foundation for the international community to explore policy directions to mitigate climate change in the upcoming post-COVID-19 period.

Evaluating the Mutual Relationship between IPAT/Kaya Identity Index and ODIAC-Based GOSAT Fossil-Fuel CO2 Flux: Potential and Constraints in Utilizing Decomposed Variables.

The IPAT/Kaya identity is the most popular index used to analyze the driving forces of individual factors on CO2 emissions. It represents the CO2 emissions as a product of factors, such as the population, gross domestic product (GDP) per capita, energy intensity of the GDP, and carbon footprint of energy. In this study, we evaluated the mutual relationship of the factors of the IPAT/Kaya identity and their decomposed variables with the fossil-fuel CO2 flux, as measured by the Greenhouse Gases Observing Satellite (GOSAT). We built two regression models to explain this flux; one using the IPAT/Kaya identity factors as the explanatory variables and the other one using their decomposed factors. The factors of the IPAT/Kaya identity have less explanatory power than their decomposed variables and comparably low correlation with the fossil-fuel CO2 flux. However, the model using the decomposed variables shows significant multicollinearity. We performed a multivariate cluster analysis for further investigating the benefits of using the decomposed variables instead of the original factors. The results of the cluster analysis showed that except for the M factor, the IPAT/Kaya identity factors are inadequate for explaining the variations in the fossil-fuel CO2 flux, whereas the decomposed variables produce reasonable clusters that can help identify the relevant drivers of this flux.

Differentiating carbon sinks versus sources on a university campus using synergistic UAV NIR and visible signatures.

This research proposes a framework for quantitatively differentiating carbon sinks versus sources, utilizing synergistic NIR (near-infrared) and visible signatures acquired from UAV (unmanned aerial vehicle). UAV NIR and visible imagery acquired at 70-m flying altitude identified the major types of carbon sinks versus sources, such as vegetation and constructed surfaces (e.g., road and buildings) for representative section of the university campus at the level of almost field-survey standpoint. Our findings show that the NIR reflectance for the sink was distributed in the range of 9.46-44.65%, whereas the emission sources had shown NIR response, ranging from 16.74 to 22.67%. The visible green reflectance showed a significantly higher range for emission sources (23.6-52.3%) than the sink (13.50-26.74%). The emission source in visible red showed a wider range of reflectance (17.05-38.49%), while the sink was observed in the narrow range of 9.36-17.75%. It was confirmed that synergistically combining NIR and visible signatures offers a viable method for measuring and comparing campus-wide carbon sinks versus sources due to extremely hyper-spatial resolution. It is anticipated that this research will be used as a valuable reference to investigate hyper-localized carbon sources and sinks in university campuses as cities within cities.

Metropolitan urban hotspots of chronic sleep deprivation: evidence from a community health survey in Gyeongbuk Province, South Korea.

The geographic concentration of chronic sleep deprivation (CSD) remains largely unexplored. This paper examined the community-specific spatial pattern of the prevalence of CSD and the presence of clustered spatial hotspots among the Korean elderly population in Gyeongbuk Province, South Korea, revealing CSD hotspots and underscoring the importance of geography-focused prevention strategies. The study analysed cross-sectional data collected from 9847 elderly individuals aged 60 years and older who participated in a Korean Community Health Survey conducted in 2012. To assess the level of spatial dependence, an exploratory spatial data analysis was conducted using Global Moran's I statistic and the local indicator of spatial association. The results revealed marked geographic variations in CSD prevalence ranging from 33.4 to 73.4%, with higher values in the metropolitan urban areas and lower in the rural areas. Almost half of the community residents [both men (44.1%) and women (53.5%)] slept 6 h or less per 24 h. The average CSD prevalence (53.6% men and 65.1% women) in the hotspots was about 13.0% higher than that in other areas (42.6% for men and 51.1% for women). To our knowledge, this is the first study to generate a CSD hotspot map that includes data on sleep deprivation across metropolitan district levels. This study demonstrates that not only is sleep deprivation distributed differentially across communities but these differences may be explained by urbanisation.