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Under dual impacts from climate change and human activities, the Yellow River Basin (YRB) of China suffers from droughts and water scarcity. Understanding variations of terrestrial water storage (TWS) over the YRB is significant and beneficial to regional water resources management and sustainable development. This study investigates TWS variations in the YRB using data sets from two solutions (RL05 and RL06) of the Gravity Recovery and Climate Experiment (GRACE) satellites, as well as from global land surface models (the NOAH model and Catchment Land Surface Model [CLSM]) and hydrology and water resources model (PCR‐GLOBWB). Annual terrestrial water storage anomalies (TWSA) variation patterns were tracked by introducing a weighed centroid analysis concept. Human water use was analyzed, and its importance to TWS changes was evaluated by using the random forest algorithm. Conclusions can be briefly summarized as follows: (1) The declining trend of TWS in the YRB from the RL06 solution is more negative than that from RL05; the TWS trend from the PCR‐GLOBWB model is more consistent with GRACE than with the NOAH model and CLSM at the basin level, and the three models all underestimate the declining trend of TWS in the midstream relative to the GRACE solutions. (2) Spatial weighed centroids of annual GRACE TWSA moved toward the headwater of the YRB during the 2003–2015 period, suggesting a wide gap of TWS between the upstream and downstream YRB; the TWSA time series well correlate with the long‐term accumulation of climatic water balance, but the connections became weak in the 2010–2015 period. (3) Groundwater withdrawals have been controlled according to the Water Resources Bulletin, but the stress may be partly shifted to surface water; the increasing trend of ecological and industrial surface water use is significant, following the agriculture water use. (4) For the entire YRB, groundwater use accounts for the majority of feature importance in modeling TWSA time series, and the contribution of climate factors is the least. Agricultural water use ranks first relative to other sectors for the YRB, followed by the ecological and industrial use. This study provides a first comparison of TWSA between the latest Center for Space Research (CSR) GRACE RL06 solution and the previous RL05 solution in the YRB. The results are expected to present a comprehensive picture of TWS variations in the YRB and the impacts from climate and human factors.

1-Butyl-3-methylimidazolium chloride ([BMIM]Cl) was selected as co-solvent to dissolve cellulose and silk fibroin and the cellulose/silk fibroin blend fibers were fabricated with dry-jet wet spinning technology. The phase morphology of cellulose and silk fibroin in the blend fibers was studied by scanning electron microcopy and laser scanning confocal microscope. It is shown that the cellulose is in the continuous phase and silk fibroin exists as ''fibril-like'' in cellulose, in which the radial dimension of silk fibroin phase is 0.5-1.0 lm. The phase size of silk fibroin along the fiber axis increased with the increase of silk fibroin content and draw ratio. From the wideangle X-ray scattering, it is found that the total crystallinity of the blend fibers decreased with increasing silk fibroin content. The hydrogen bond between cellulose and silk fibroin was observed from Fourier transform infrared spectra. Although the tensile strength and initial modulus of blend fibers decreased with increasing silk fibroin content, the tensile strength of blend fibers contain 35 wt% silk fibroin was up to 191 MPa.

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Satellite retrieved soil moisture (SM) shows great potential in hydrological, meteorological, ecological, and agricultural applications, while the coarse resolution limits its utilization in regional scale. The regression tree-based machine learning algorithms reveal promising capability in SM downscaling. However, it lacks systematic study dedicated to intercomparisons of algorithms to explicitly illuminate their characteristics. In this study, comparisons are made to systematically evaluate performances of classification and regression tree (CART), random forest (RF), gradient boost decision tree (GBDT), and extreme gradient boost (XGB) in Soil Moisture Active Passive (SMAP) SM downscaling in southwest France. The results show that the four algorithms downscaled SM are capable of capturing spatial distribution features of the original SMAP SM. The downscaled regions with favorable accuracy are mostly situated in the dominant Mediterranean climate zone with moderate vegetation coverage and mild topography variation. The best results are obtained by GBDT in grassland with R value of 0.77 and ubRMSE value of 0.04 m 3 /m 3. The RF and XGB also achieve good performances. On the whole, the GBDT approach is robust and reliable, which could downscale SM with superior correlation and smaller bias than the others. Besides, it achieves higher accuracy than the original SMAP in grassland and shrubland. The feature importance index of each explainable variable fluctuates regularly among different seasons and models. This study proves the outstanding performance of GBDT in SMAP SM downscaling and is expected to act as a valuable reference for studies focusing on SM scale conversion algorithms.

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Fine particulate matter (PM 2.5) has been manifested to be one of the major health-threatening airborne pollutants in the urban environment, as it is composed of inhalable particles, which may have considerable adverse health effects on the human respiratory system. However, there is limited evidence on the difference in these effects among various population groups in China. This paper aimed to perform a comparative analysis on the health effect of PM 2.5 on hospital admissions of acute respiratory infections (ARI) for both children and adults. Total 39 604 hospital admission records were collected from 98 hospitals and then matched with air pollution data from 19 monitoring stations from January 1 to December 31 in 2014. A spatial correlation test was used to estimate the spatial dependency and a time series analysis designed with a distributed lag non-linear model was further involved to evaluate the associations between PM 2.5 pollution and ARI admissions. Significant effect distinctions were detected between children and adults, mainly revealing in acute lower respiratory infections (ALRI). For ALRI, the spatial correlation coefficient between population exposure and hospital admissions was 0.69 for children and 0.34 for adults. Meanwhile, for children, significant associations between PM 2.5 and ALRI admissions were found at quite low concentrations (slightly above zero) and lasted for six days, with each 10-µg/m 3 increase in PM 2.5 corresponded to a 4.3% (95%CI: 1.2%, 7.2%) increase in the number of admissions. While for adults, no significant association emerged until the concentration of PM 2.5 exceeded a threshold value (100 µg/m 3 for the lag of two days) and lasted no more than three days. Our results suggested that short-term exposures to PM 2.5 were associated with increased risk of ALRI admissions for children and adults in various ways, and emphasized the needs for specific preventive measures for different age groups. INDEX TERMS Acute respiratory infection, children, particulate matter, hospital admission, lag effect. The associate editor coordinating the review of this manuscript and approving it for publication was Vijay Mago.