Taylor Reynolds (tableman0)

The batch kinetic experiments showed that the adsorption of QDNPs followed first- and second-order kinetic interactions at low and high ISs, respectively. These results indicate that the well-known colloid filtration theory that assumes irreversible first-order kinetics for colloid deposition is not suitable for describing the QDNP adsorption. The findings in our work can aid better description and prediction of fate and transport of QDNPs in subsurface environments.Accurate calculation of the longitudinal dispersion coefficient (Kx) of pollution is essential in modeling river pollution status. Various equations are presented to calculate the Kx using experimental, analytical, and mathematical methods. Although machine learning models are more reliable than experimental equations in the presence of uncertainties missing data, they have not been widely used in predicting Kx. In this study, the Kx of the river was predicted using machine learning methods, including least square-support vector machine (LS-SVM), adaptive neuro-fuzzy inference system (ANFIS), and ANFIS optimized by Harris hawk optimization (ANFIS-HHO), and the results were compared with that of the experimental methods. Several scenarios were designed by different combinations of input variables, such as the average depth of the flow (H), average flow velocity (U), and shear velocity (u⁎). The results showed that machine learning models had a more efficient performance to predict Kx than experimental equations. The ANFIS-HHO, with a scenario containing all the input variables, performed better than the other two models, with root mean square error, mean absolute percentage error, and coefficient of determination of 17.0, 0.22, and 0.97, respectively. Furthermore, the HHO algorithm slightly increased the prediction performance of the ANFIS. The discrepancy ratio (DR) evaluation criteria showed that experimental equations overestimated the values of Kx, while the machine learning models resulted in higher precision. Also, the results of Taylor's diagram showed the acceptable performance of the ANFIS-HHO model compared to other models. Given the promising results of the present study, it is expected that the proposed approach can be efficiently used for similar environmental modeling problems.Thermal use of the shallow subsurface and its aquifers ( less then 400 m) is steadily increasing. Currently, more than 2800 aquifer thermal energy storage (ATES) systems are operating worldwide alongside more than 1.2 million ground source heat pump (GSHP) systems in Europe alone. These rising numbers of shallow geothermal energy (SGE) systems will put additional pressure on typically vulnerable groundwater systems. Hitherto, suitable criteria to control the thermal use of groundwater in national and international legislations are often still at a preliminary state or even non-existing. While the European Union (EU) Water Framework Directive (WFD) defined the release of heat into the groundwater as pollution in the year 2000, the cooling of groundwater for heating purposes is not explicitly mentioned yet. In contrast, some national legislations have stricter guidelines. For example, in Germany, detrimental changes in physical, chemical and biological characteristics have to be avoided. In the Swiss water ordroduction and thermal use.Silicon (Si) is a beneficial macronutrient for plants. The Si supplementation to growth media mitigates abiotic and biotic stresses by regulating several physiological, biochemical and molecular mechanisms. The uptake of Si from the soil by root cells and subsequent transport are facilitated by Lsi1 (Low silicon1) belonging to nodulin 26-like major intrinsic protein (NIP) subfamily of aquaporin protein family, and Lsi2 (Low silicon 2) belonging to putative anion transporters, respectively. The soluble Si in the cytosol enhances the production of jasmonic acid, enzymatic and non-enzymatic antioxidants, secondary metabolites and induces expression of