Decoupling of economic growth from CO₂ emissions in Yangtze River Economic Belt cities

Cities play significant roles in mitigating global climate change and formulating low carbon roadmaps. As the first regional strategy that prioritizes green development, the Yangtze River Economic Belt (YREB) is an economic circle along the Yangtze River, stringing up 11 provinces and municipalities from west to east of China. The huge regional heterogeneity in terms of economic development, size, and structure in YREB cities need differentiated emission reduction strategies and low-carbon development pathways. This study compiled the CO2 emission inventories of 85 cities in the YREB for the first time and explored the decoupling of economic growth from CO2 emissions at the city level. The results show that CO2 emissions of YREB cities increased at an annual average rate of 5.1% from 2005 to 2017, and 85 YREB cities emitted 44% of national total CO2 emissions and contributed 41% of national GDP in 2017. 61% of cities dominated by high-tech and service industry achieved decoupling between economic development and emissions before 2009 and are moving forward to a stronger decoupling state. 25% of cities achieved decoupling after 2009 and these post-decoupling cities took the heavy industry and light industry as their leading industries. Resource-based cities with slow economic development and high CO2 emissions changed from decoupling to negative decoupling or coupling. The proposed differentiated low-carbon development pathways for YREB cities could provide references for cities at different stages to achieve decoupling of GDP from CO2 emissions and emission reduction goals

Particle velocity profile in an inclined rotating drum

Various experimental and numerical studies have been carried out to study the velocity profiles of the particles inside horizontal rotating drums, but little emphasis has been laid on inclined rotating drums, though these drums are extensively used in granular process industries. In this study, velocity profiles of the particles in a rotating drum with 0o, 5o, 10o, and 15o inclinations have been studied by using the discrete element method. It was found that at the locations of the drum with the volumetric fill in the range of 0.2 0.8, the inclined rotating drum has a similar variation of velocity with the radial height to the horizontal rotating drum. However, the variation of the average particle velocity along the length of the drum differs for horizontal and inclined drums. Furthermore, for the inclined rotating drum, the average velocity increases with the increase in the volumetric fill

Modeling rules of regional flash flood susceptibility prediction using different machine learning models

The prediction performance of several machine learning models for regional flash flood susceptibility is characterized by variability and regionality. Four typical machine learning models, including multilayer perceptron (MLP), logistic regression (LR), support vector machine (SVM), and random forest (RF), are proposed to carry out flash flood susceptibility modeling in order to investigate the modeling rules of different machine learning models in predicting flash flood susceptibility. The original data of 14 environmental factors, such as elevation, slope, aspect, gully density, and highway density, are chosen as input variables for the MLP, LR, SVM, and RF models in order to estimate and map the distribution of the flash flood susceptibility index in Longnan County, Jiangxi Province, China. Finally, the prediction performance of various models and modeling rules is evaluated using the ROC curve and the susceptibility index distribution features. The findings show that: 1) Machine learning models can accurately assess the region’s vulnerability to flash floods. The MLP, LR, SVM, and RF models all predict susceptibility very well. 2) The MLP (AUC=0.973, MV=0.1017, SD=0.2627) model has the best prediction performance for flash flood susceptibility, followed by the SVM (AUC=0.964, MV=0.1090, SD=0.2561) and RF (AUC=0.975, MV=0.2041, SD=0.1943) models, and the LR (AUC=0.882, MV=0.2613, SD=0.2913) model. 3) To a large extent, environmental factors such as elevation, gully density, and population density influence flash flood susceptibility

Early Cretaceous high-Ti and low-Ti mafic magmatism in Southeastern Tibet: Insights into magmatic evolution of the Comei Large Igneous Province

The Dala diabase intrusion, at the southeastern margin of the Yardoi gneiss dome, is located within the outcrop area of the ~ 132 Ma Comei Large Igneous Province (LIP), the result of initial activity of the Kerguelen plume. We present new zircon U-Pb geochronology results to show that the Dala diabase was emplaced at ~ 132 Ma and geochemical data (whole-rock element and Sr-Nd isotope ratios, zircon Hf isotopes and Fe-Ti oxide mineral chemistry) to confirm that the Dala diabase intrusion is part of the Comei LIP. The Dala diabase can be divided into a high-Mg/low-Ti series and a low-Mg/high-Ti series. The high-Mg/low-Ti series represents more primitive mafic magma compositions that we demonstrate are parental to the low-Mg/high-Ti series. Fractionation of olivine and clinopyroxene, followed by plagioclase within the low-Mg series, lead to systematic changes in concentrations of mantle compatible elements (Cr, Co, Ni, and V), REEs, HFSEs, and major elements such as Ti and P. Some Dala samples from the low-Mg/high-Ti series contain large ilmenite clusters and show extreme enrichment of Ti with elevated Ti/Y ratios, likely due to settling and accumulation of ilmenite during the magma chamber evolution. However, most samples from throughout the Comei LIP follow the Ti-evolution trend of the typical liquid line of descent (LLD) of primary OIB compositions, showing strong evidence of control of Ti contents by differentiation processes. In many other localities, however, primitive magmas are absent and observed Ti contents of evolved magmas cannot be quantitatively related to source processes. Careful examination of the petrogenetic relationship between co-existing low-Ti and high-Ti mafic rocks is essential to using observed rock chemistry to infer source composition, location, and degree of melting