A novel causal structure-based framework for comparing a basin-wide water–energy–food–ecology nexus applied to the data-limited Amu Darya and Syr Darya river basins

The previous comparative studies on watersheds were mostly based on the comparison of dispersive characteristics, which lacked systemicity and causality. We proposed a causal structure-based framework for basin comparison based on the Bayesian network (BN) and focus on the basin-scale water–energy–food–ecology (WEFE) nexus. We applied it to the Syr Darya River basin (SDB) and the Amu Darya River basin (ADB), of which poor water management caused the Aral Sea disaster. The causality of the nexus was effectively compared and universality of this framework was discussed. In terms of changes in the nexus, the sensitive factor for the water supplied to the Aral Sea changed from the agricultural development during the Soviet Union period to the disputes in the WEFE nexus after the disintegration. The water–energy contradiction of the SDB is more severe than that of the ADB, partly due to the higher upstream reservoir interception capacity. It further made management of the winter surplus water downstream of the SDB more controversial. Due to this, the water–food–ecology conflict between downstream countries may escalate and turn into a long-term chronic problem. Reducing water inflow to depressions and improving the planting structure prove beneficial to the Aral Sea ecology, and this effect of the SDB is more significant. The construction of reservoirs on the Panj River of the upstream ADB should be cautious to avoid an intense water–energy conflict such as the SDB's. It is also necessary to promote the water-saving drip irrigation and to strengthen the cooperation

Full Hierarchic Versus Non-Hierarchic Classification Approaches for Mapping Sealed Surfaces at the Rural-Urban Fringe Using High-Resolution Satellite Data

Since 2008 more than half of the world population is living in cities and urban sprawl is continuing. Because of these developments, the mapping and monitoring of urban environments and their surroundings is becoming increasingly important. In this study two object-oriented approaches for high-resolution mapping of sealed surfaces are compared: a standard non-hierarchic approach and a full hierarchic approach using both multi-layer perceptrons and decision trees as learning algorithms. Both methods outperform the standard nearest neighbour classifier, which is used as a benchmark scenario. For the multi-layer perceptron approach, applying a hierarchic classification strategy substantially increases the accuracy of the classification. For the decision tree approach a one-against-all hierarchic classification strategy does not lead to an improvement of classification accuracy compared to the standard all-against-all approach. Best results are obtained with the hierarchic multi-layer perceptron classification strategy, producing a kappa value of 0.77. A simple shadow reclassification procedure based on characteristics of neighbouring objects further increases the kappa value to 0.84

Metal-organic framework templated electrodeposition of functional gold nanostructures

Utilizing a pair of quick, scalable electrochemical processes, the permanently porous MOF HKUST-1 was electrochemically grown on a copper electrode and this HKUST-1-coated electrode was used to template electrodeposition of a gold nanostructure within the pore network of the MOF. Transmission electron microscopy demonstrates that a proportion of the gold nanostructures exhibit structural features replicating the pore space of this ∼1.4 nm maximum pore diameter MOF, as well as regions that are larger in size. Scanning electron microscopy shows that the electrodeposited gold nanostructure, produced under certain conditions of synthesis and template removal, is sufficiently inter-grown and mechanically robust to retain the octahedral morphology of the HKUST-1 template crystals. The functionality of the gold nanostructure within the crystalline HKUST-1 was demonstrated through the surface enhanced Raman spectroscopic (SERS) detection of 4-fluorothiophenol at concentrations as low as 1 μM. The reported process is confirmed as a viable electrodeposition method for obtaining functional, accessible metal nanostructures encapsulated within MOF crystals

Coordination Polymer Flexibility Leads to Polymorphism and Enables a Crystalline Solid-Vapour Reaction: A Multi-technique Mechanistic Study.

Despite an absence of conventional porosity, the 1D coordination polymer [Ag4 (O2 C(CF2 )2 CF3 )4 (TMP)3 ] (1; TMP=tetramethylpyrazine) can absorb small alcohols from the vapour phase, which insert into AgO bonds to yield coordination polymers [Ag4 (O2 C(CF2 )2 CF3 )4 (TMP)3 (ROH)2 ] (1-ROH; R=Me, Et, iPr). The reactions are reversible single-crystal-to-single-crystal transformations. Vapour-solid equilibria have been examined by gas-phase IR spectroscopy (K=5.68(9)×10(-5) (MeOH), 9.5(3)×10(-6) (EtOH), 6.14(5)×10(-5) (iPrOH) at 295 K, 1 bar). Thermal analyses (TGA, DSC) have enabled quantitative comparison of two-step reactions 1-ROH→1→2, in which 2 is the 2D coordination polymer [Ag4 (O2 C(CF2 )2 CF3 )4 (TMP)2 ] formed by loss of TMP ligands exclusively from singly-bridging sites. Four polymorphic forms of 1 (1-A(LT) , 1-A(HT) , 1-B(LT) and 1-B(HT) ; HT=high temperature, LT=low temperature) have been identified crystallographically. In situ powder X-ray diffraction (PXRD) studies of the 1-ROH→1→2 transformations indicate the role of the HT polymorphs in these reactions. The structural relationship between polymorphs, involving changes in conformation of perfluoroalkyl chains and a change in orientation of entire polymers (A versus B forms), suggests a mechanism for the observed reactions and a pathway for guest transport within the fluorous layers. Consistent with this pathway, optical microscopy and AFM studies on single crystals of 1-MeOH/1-A(HT) show that cracks parallel to the layers of interdigitated perfluoroalkyl chains develop during the MeOH release/uptake process

A global meta-analysis of soil salinity prediction integrating satellite remote sensing, soil sampling, and machine learning

Despite the growing interest among researchers, satellite-based prediction of soil salinity remains highly uncertain. The improvements in prediction accuracy reported in previous studies are usually limited to a single area. We performed a meta-analysis of regional satellite-based soil salinity predictions combined with in situ soil sampling and machine learning. Based on R-2 and root-mean-square error (RMSE) collected, we evaluated the effects of various features on the model accuracy and established a Bayesian network to evaluate the joint causal effect of multifeatures. Most significant differences were found in soil sampling schemes and characteristics of the study area, including the mean and variability (averaged R-2 of 0.75 for soil sample sets with lower salinity variation and 0.62 for others) of the salinity, climate type (R-2 of 0.64 in arid areas and 0.74 in others), soil texture (R-2 of 0.66 in sandy areas and 0.57 in others), and the interval between sampling date and satellite data acquisition date (R-2 of 0.53 under the condition of over 15 days and 0.65 in others). Generally, using different satellite data has limited effects on model performance among which Sentinel-2 performed better (R-2 = 0.72) than Landsat (R-2 = 0.66). The sampling of subsamples for each sample should focus on their subpixel-scale spatial heterogeneity across satellite data rather than the number of subsamples. It is also necessary to select appropriate vegetation and salinity indices for different satellite data under different vegetation conditions. Among algorithms, random forests (R-2 = 0.70) and support vector machines (R-2 = 0.71) performed best