Chemistry of Neutral and Alkaline Waters with Low Al<Superscript>3+</Superscript> Activity Against Hydroxyaluminosilicate HAS<Subscript>B</Subscript> Solubility. The Evidence from Ground and Surface Waters of the Sudetes Mts. (SW Poland)

A wide set of aqueous chemistry data (574 water analyses) from natural environments has been used to testify and validate of the solubility of synthetic hydroxyaluminosilicate (HAS_B)_, Al₂Si₂O₅(OH)₄. The ground and surface waters represent regolith and/or fissure aquifers in various (magmatic, sedimentary and metamorphic) bedrocks in the Sudetes Mts. (SW Poland). The solubility of HAS_B in natural waters was calculated using the method proposed by Schneider et al. (Polyhedron 23:3185–3191, 2004). Results confirm usefulness and validity of this method. The HAS_B solubility obtained from the field data (logK_sp = −44.7 ± 0.58) is lower than it was estimated (logK_sp = −40.6 ± 0.15) experimentally (Schneider et al. Polyhedron 23:3185–3191, 2004). In the waters studied the equilibrium with HAS_B is maintained at pH above 6.7 and at [Al³⁺] ≤ 10⁻¹⁰. Silicon activity (log[H₄SiO₄]) ranges between −4.2 and −3.4. Due to the calculation method used, the K_sp mentioned above cannot be considered as a classical solubility constant. However, it can be used in the interpretation of aluminium solubility in natural waters. The HAS_B has solubility lower than amorphous Al(OH)₃, and higher than proto-imogolite. From water samples that are in equilibrium with respect to HAS_B, the solubility product described by the reaction, is calculated to be logK_sp = 14.0 (±0.7) at 7°C.     

Determination of Surface Precipitation Type Based on the Data Fusion Approach

Hazardous events related to atmospheric precipitation depend not only on the intensity of surface precipitation,but also on its type.Uncertainty related to determination of the precipitation type(PT)leads to financial losses in many areas of human activity,such as the power industry,agriculture,transportation,and many more.In this study,we use machine learning(ML)algorithms with the data fusion approach to more accurately determine surface PT.Based on surface synoptic observations,ERA5 reanalysis,and radar data,we distinguish between liquid,mixed,and solid precipitation types.The study domain considers the entire area of Poland and a period from 2015 to 2017.The purpose of this work is to address the question:“How can ML techniques applied in observational and NWP data help to improve the recognition of the surface PT?”Despite testing 33 parameters,it was found that a combination of the near-surface air temperature and the depth of the warm layer in the 0-1000 m above ground level(AGL)layer contains most of the signal needed to determine surface PT.The accrued probability of detection for liquid,solid,and mixed PTs according to the developed Random Forest model is 98.0%,98.8%,and 67.3%,respectively.The application of the ML technique and data fusion approach allows to significantly improve the robustness of PT prediction compared to commonly used baseline models and provides promising results for operational forecasters.