Using measurements of the column-averaged CO₂ dry air mole fraction (XCO₂) from GOSAT and biosphere parameters, including normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), leaf area index (LAI), gross primary production (GPP), and land surface temperature (LST) from MODIS, this study proposes a data-driven approach to assess the impacts of terrestrial biosphere activities on the seasonal cycle pattern of XCO₂. A unique global land mapping dataset of XCO₂ with a resolution of 1° by 1° in space, and three days in time, from June 2009 to May 2014, which facilitates the assessment at a fine scale, is first produced from GOSAT XCO₂ retrievals. We then conduct a statistical fitting method to obtain the global map of seasonal cycle amplitudes (SCA) of XCO₂ and NDVI, and implement correlation analyses of seasonal variation between XCO₂ and the vegetation parameters. As a result, the spatial distribution of XCO₂ SCA decreases globally with latitude from north to south, which is in good agreement with that of simulated XCO₂ from CarbonTracker. The spatial pattern of XCO₂ SCA corresponds well to the vegetation seasonal activity revealed by NDVI, with a strong correlation coefficient of 0.74 in the northern hemisphere (NH). Some hotspots in the subtropical areas, including Northern India (with SCA of 8.68 ± 0.49 ppm on average) and Central Africa (with SCA of 8.33 ± 0.25 ppm on average), shown by satellite measurements, but missed by model simulations, demonstrate the advantage of satellites in observing the biosphere–atmosphere interactions at local scales. Results from correlation analyses between XCO₂ and NDVI, EVI, LAI, or GPP show a consistent spatial distribution, and NDVI and EVI have stronger negative correlations over all latitudes. This may suggest that NDVI and EVI can be better vegetation parameters in characterizing the seasonal variations of XCO₂ and its driving terrestrial biosphere activities. We, furthermore, present the global distribution of phase lags of XCO₂ compared to NDVI in seasonal variation, which, to our knowledge, is the first such map derived from a completely data-driven approach using satellite observations. The impact of retrieval error of GOSAT data on the mapping data, especially over high-latitude areas, is further discussed. Results from this study provide reference for better understanding the distribution of the strength of carbon sink by terrestrial ecosystems and utilizing remote sensing data in assessing the impact of biosphere–atmosphere interactions on the seasonal cycle pattern of atmospheric CO₂ columns. Full article

Burn depth assessment in clinics is still inaccurate because of the lack of feasible and practical testing devices and methods. Therefore, this process often depends on subjective judgment of burn surgeons. In this study, a new unilateral magnetic resonance imaging (UMRI) sensor equipped with a 2D gradient coil system was established, and we attempted to assess burns using unilateral nuclear magnetic resonance devices. A reduced Halbach magnet was utilized to generate a magnetic field that was relatively homogeneous on a target plane with a suitable field of view for 2D spatial localization. A uniplanar gradient coil system was designed by utilizing the mainstream target field method, and a uniplanar RF (radio frequency) coil was designed by using a timeharmonic inverse method for the UMRI sensor. A 2D image of the cross sections of a simple burn model was obtained by a fast 2D pure-phase encoding imaging method. The design details of the novel single-sided MRI probe and imaging tests are also presented. Full article

Over the past three decades, China’s economy has witnessed remarkable growth, with an average annual growth rate over 9%. However, China also faces great challenges to balance this spectacular economic growth and continuously increasing energy use like many other economies in the world. With the aim of designing effective energy and environmental policies, policymakers are required to master the relationship between energy consumption and economic growth. Therefore, in the case of North China, a multivariate model employing panel data analysis method based on the Cobb-Douglas production function which introduces electricity consumption as a main factor was established in this paper. The equilibrium relationship and causal relationship between real GDP, electricity consumption, total investment in fixed assets, and the employment were explored using data during the period of 1995–2014 for six provinces in North China, including Beijing City, Tianjin City, Hebei Province, Shanxi Province, Shandong Province and Inner Mongolia. The results of panel co-integration tests clearly state that all variables are co-integrated in the long term. Finally, Granger causality tests were used to examine the causal relationship between economic growth, electricity consumption, labor force and capital. From the Granger causality test results, we can draw the conclusions that: (1) There exist bi-directional causal relationships between electricity consumption and real GDP in six provinces except Hebei; and (2) there is a bi-directional relationship between capital input and economic growth and between labor force input and economic growth except Beijing and Hebei. Therefore, the ways to solve the contradiction of economic growth and energy consumption in North China are to reduce fossil energy consumption, develop renewable and sustainable energy sources, improve energy efficiency, and increase the proportion of the third industry, especially the sectors which hold the characteristics of low energy consumption and high value-added. Full article