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.

Tiling motion patches.

Simulating multiple character interaction is challenging because character actions must be carefully coordinated to align their spatial locations and synchronized with each other. We present an algorithm to create a dense crowd of virtual characters interacting with each other. The interaction may involve physical contacts, such as hand shaking, hugging, and carrying a heavy object collaboratively. We address the problem by collecting deformable motion patches, each of which describes an episode of multiple interacting characters, and tiling them spatially and temporally. The tiling of motion patches generates a seamless simulation of virtual characters interacting with each other in a nontrivial manner. Our tiling algorithm uses a combination of stochastic sampling and deterministic search to address the discrete and continuous aspects of the tiling problem. Our tiling algorithm made it possible to automatically generate highly complex animation of multiple interacting characters. We achieve the level of interaction complexity far beyond the current state of the art that animation techniques could generate, in terms of the diversity of human behaviors and the spatial/temporal density of interpersonal interactions.