Soil particle size fractions (PSFs) are important properties for understanding the physical and chemical processes in soil systems. Knowledge about the distribution of soil PSFs is critical for sustainable soil management. Although log-ratio transformations have been widely applied to soil PSFs prediction, the statistical distribution of original data and the transformed data given by log-ratio transformations is different, resulting in biased estimates of soil PSFs. Therefore, multivariate random forest (MRF) was utilized for the simultaneous prediction of soil PSFs, as it is able to capture dependencies and internal relations among the three components. Specifically, 243 soil samples collected across the Loess Plateau were used. Meanwhile, Landsat data, terrain attributes, and climatic variables were employed as environmental variables for spatial prediction of soil PSFs. The results depicted that MRF gave satisfactory soil PSF prediction performance, where the R² values were 0.62, 0.53, and 0.73 for sand, silt, and clay, respectively. Among the environmental variables, nighttime land surface temperature (LST_N) presented the highest importance in predicting soil PSFs in the Loess Plateau, China. Maps of soil PSFs and texture were generated at a 30 m resolution, which can be utilized as alternative data for soil erosion management and ecosystem conservation.

Highly efficient removal of phosphate from water bodies is of great importance to controlling eutrophication. Herein, the lanthanum-based perovskites (i.e., LaMnO3, LaFeO3) were synthesized and employed for phosphorus adsorption. Adsorption experiments showed that the phosphate removal by LaMnO3 was well fitted with the pseudo second-order kinetic model, with a multi-stage adsorption process. The LaMnO3 exhibited a maximum adsorption capacity of 51.3 mg/g for phosphate calculated using the Langmuir model, which was 2.1 times higher than that of LaFeO3 under neutral conditions. Characterization and DFT calculation results further revealed that the La element on the surface of perovskite was the main adsorption site via inner-sphere complex and/or electrostatic interactions, whereas the B-site metal (i.e. Mn element) could trigger the surface electronic structure modulation such as the reduction of surface bonding barrier, for further improving the phosphate adsorption. Additionally, the LaMnO3 adsorbent exhibited commendable performance in treating natural water with low phosphorus concentration and good regenerative capability. This work provides insight into the development of novel perovskite-type adsorbents for efficiently removing phosphorus in environmental remediation.