Using the reductive perturbation method, a Korteweg-de Vries (KdV) equation is derived to study the nonlinear properties of electrostatic collisionless dust acoustic solitons in pair-ion-electron (p-i-e) plasmas. The fluid model is chosen for positive ions, negative ions, and the fraction of electrons and charged (both positive and negative) dynamic dust particles. It is realized that electrostatic hump structures can be found when the dust particles are positively charged, and electrostatic dip structures can be detected for negatively charged dust particles. Numerical solutions for these dust acoustic solitons are plotted and their characteristics are discussed. It is found that the amplitude and width of the electrostatic dust acoustic solitons increase when the density of the dust particles and/or the temperature of the negative ions increases, and that the amplitude and width of these solitons decrease when the temperature of the positive ions increases. As pair-ion plasmas mimic electron-positron plasmas, our results might be helpful in understanding the nonlinear dust acoustic solitary waves in super dense astronomical bodies like neutron stars.

Core Ideas Conventional methods for measuring CEC are time consuming and costly. A Vis–NIRS model and pedotransfer function (PTF) were developed for CEC determination. The Vis–NIRS model estimated CEC accurately for different soil types. The Vis–NIRS CEC model performed better than the PTF based on clay and organic C contents. Knowledge of the cation exchange capacity (CEC) for soils or other porous media is very important for civil engineering and agricultural applications. However, the standard laboratory methods to measure CEC are costly and laborious. The aim of this research was to develop a visible–near‐infrared spectroscopy (Vis–NIRS, 400–2500 nm) calibration model to predict CEC based on multivariate analysis and to compare the predictive ability of Vis–NIRS with that of a pedotransfer function (PTF). For this purpose, reference CEC was measured by the ammonium acetate method for 235 soil samples, collected from 21 countries. Diffuse spectral reflectance data were also collected by using a NIRSTM DS2500 spectrometer. The model was constructed on a calibration subset (80%) and evaluated with a validation subset (20%) using partial least squares regression. The Vis–NIRS calibration model was sufficiently robust based on the cross‐validation results [R2 = 0.79, RMSE of cross‐validation values of 7.9 cmolc kg−1 and bias = −0.14]. The independent validation of the Vis–NIRS model showed good prediction accuracy, regardless of sample origin (RMSE of prediction value of 5.0 cmolc kg−1 and ratio of performance to interquartile distance value of 4.5). Moreover, the Vis–NIRS prediction performance was superior to that of the PTF, which was influenced by the sample origin (RMSE values of 11.5 cmolc kg−1). The better prediction of CEC by the Vis–NIRS calibration model suggests that it is due to the co‐variation of CEC with clay (type and content) and organic C content.

The soil specific surface area (SSA) is a fundamental property governing a range of soil processes relevant to engineering, environmental, and agricultural applications. A method for SSA determination based on a combination of visible nearinfrared spectroscopy (vis-NIRS) and vapor sorption isotherm measurements was proposed. Two models for water vapor sorption isotherms (WSIs) were used: the Tuller-Or (TO) and the Guggenheim-Anderson-de Boer (GAB) model. They were parameterized with sorption isotherm measurements and applied for SSA estimation for a wide range of soils (N = 270) from 27 countries. The generated vis-NIRS models were compared with models where the SSA was determined with the ethylene glycol monoethyl ether (EGME) method. Different regression techniques were tested and included partial least squares (PLS), support vector machines (SVM), and artificial neural networks (ANN). The effect of dataset subdivision based on EGME values on model performance was also tested. Successful calibration models for SSA TO and SSA GAB were generated and were nearly identical to that of SSA EGME. The performance of models was dependent on the range and variation in SSA values. However, the comparison using selected validation samples indicated no significant differences in the estimated SSA TO , SSA GAB , and SSA EGME , with an average standardized RMSE (SRMSE = RMSE/range) of 0.07, 0.06 and 0.07, respectively. Small differences among the regression techniques were found, yet SVM performed best. The results of this study indicate that the combination of vis-NIRS with the WSI as a reference technique for vis-NIRS models provides SSA estimations akin to the EGME method.