An empirical approach to predict regional organic carbon in deep soils

Deep soil organic carbon(SOC)plays an important role in carbon cycling.Precisely predicting deep SOC at the regional scale is crucial for the accurate assessment of carbon sequestration potential in soils but has been challenging for a century.Herein,we developed a depth distribution function-based empirical approach to predict SOC in deep soils at the regional scale.We validated this approach with a dataset from four regions of the world and examined the application of this approach in China’s Loess Plateau.We found that among the reported depth distribution functions describing vertical patterns of SOC,the negative exponential function performed best in fitting SOC along the soil profile in various regions.Moreover,the parameters(i.e.,Ceand k)of the negative exponential function were linearly correlated to surface SOC(0–20 cm)and the changing rates of SOC within the topsoil(0–40 cm).Based on the above relationships,the empirical equations for predicting the negative exponential parameters are established.The validation results from site-specific and regional dataset showed that combining the negative exponential function and such empirical equations can precisely predict SOC concentration in soils down to 500 cm depth.Our study provides a simple,rapid and accurate method for predicting deep soil SOC at the regional scale,which could simplify the assessment of deep soil SOC in various regions.

A new empirical chart for rockburst analysis in tunnelling:Tunnel rockburst classification(TRC)

Rockburst is defined as a phenomenon with immediate dynamic instability under excavation unloading conditions of deep or high geostress areas.Inadequate knowledge and lack of characterizing information prevent engineers and experts from achieving appropriate prediction results related to the rockburst behaviour.In this study,a data set including 220 rockburst instances was collected for rockburst classification via the geostatistical method.An update of the 2D graph,the tunnel rockburst classification(TRC)chart,was introduced based on analysing three indicators,namely,elastic energy index(Wet),tangential stress in rock mass(σ_(0)),and uniaxial compressive strength(σ_(c)).Distribution and correlation of data were drawn on 2D plot,and the boundaries of rockburst were distinguished according to the achieved interpolate points by kriging method.Hierarchically,the validation phase was performed using an additional set of 28 case histories obtained from several projects around the world.The results showed that the TRC chart with an average error percentage of 3.6%in the prediction of rockburst had a significant and effective implementation in comparison to the exiting heuristic systems.Despite the initial character of the prediction,the described chart may be a helpful tool in the first steps of design and construction.

Reviewed Work:A Study of Liu Yong and His Lyrics,by Laifong Leung.Beijing:China Translation&Publishing House,2020.PP x+506.￥98.00(paper back).ISBN 978-7-5001-6326-8

Laifong Leung,based on the literature and materials from Chinese,English and Japanese,by using empirical approach and closing reading,produced Chinese-English combined version A Study of Liu Yong and His Lyrics.The work mainly probes into Liu Yong’s life story and closely analyzes the characteristics of his lyrics in the way of tune titles,thematic matters,images and diction,rhythm and structure,and his contributions to the establishment of Ci genre are dealt with.Therefore,it is rewarding to promote academic exchange and disseminate classical Chinese literature,and it is pioneering to provide a new angle in the study of Liu Yong and his lyrics by using quantitative analysis.