Combinations of multivariate statistical analysis and analytical hierarchical process for indexing surface water quality under arid conditions

Single-factor analysis of Ni-B-AC-catalyzed β-pinene hydrogenation based on hierarchical analysis

In the choice of catalysts for the hydrogenation of pinene, nickel-based catalysts show intriguing activity. Here, a Ni-B catalyst supported on activated carbon with Ni as an active component was synthesized by the titration reduction co-impregnation method. The mechanism of such heterogeneous systems has not yet been articulated, and the industrial applications of the potassium borohydride reduction of nickel-based catalysts are limited by their easy agglomeration and poor stability. The materials were analyzed by hierarchical and DFT studies, in situ XPS, BET, XRD, and SEM, which provided insights into the kind of signals in Ni²⁺ reduction to Ni⁰. The hierarchical analysis indicated that Ni/AC (0.4876) and reaction pressure (0.6066) influenced the catalyst preparation and process efficiency changes, respectively. Activated carbon was shown to provide a favorable basis for the stability of the Ni-B activity. In addition, the hierarchical analysis method provides new insights into the data analysis for chemical experiments.

Multi-Indicator and Geospatial Based Approaches for Assessing Variation of Land Quality in Arid Agroecosystems

Novel spatial models for appraising arable land resources using data processing techniques can increase insight into agroecosystem services. Hence, the principal component analysis (PCA), hierarchal cluster analysis (HCA), analytical hierarchy process (AHP), fuzzy logic, and geographic information system (GIS) were integrated to zone and map agricultural land quality in an arid desert area (Matrouh Governorate, Egypt). Satellite imageries, field surveys, and soil analyses were employed to define eighteen indicators for terrain, soil, and vegetation qualities, which were then reduced through PCA to a minimum data set (MDS). The original and MDS were weighted by AHP through experts’ opinions. Within GIS, the raster layers were generated, standardized using fuzzy membership functions (linear and non-linear), and assembled using arithmetic mean and weighted sum algorithms to produce eight land quality index maps. The soil properties (pH, salinity, organic matter, and sand), slope, surface roughness, and vegetation could adequately express the land quality. Accordingly, the HCA could classify the area into eight spatial zones with significant heterogeneity. Selecting salt-tolerant crops, applying leaching fraction, adopting sulfur and organic applications, performing land leveling, and using micro-irrigation are the most recommended practices. Highly significant (p < 0.01) positive correlations occurred among all the developed indices. Nevertheless, the coefficient of variation (CV) and sensitivity index (SI) confirmed the better performance of the index developed from the non-linearly scored MDS and weighted sum model. It could achieve the highest discrimination in land qualities (CV > 35%) and was the most sensitive (SI = 3.88) to potential changes. The MDS within this index could sufficiently represent TDS (R2 = 0.88 and Kappa statistics = 0.62), reducing time, effort, and cost for estimating the land performance. The proposed approach would provide guidelines for sustainable land-use planning in the studied area and similar regions.