River water quality monitoring is crucial for understanding water dynamics and formulating policies to conserve the water environment. In situ ultraviolet–visible (UV–Vis) spectrometry holds great potential for real-time monitoring of multiple water quality parameters. However, establishing a reliable methodology to link absorption spectra to specific water quality parameters remains challenging, particularly for eutrophic rivers under various flow and water quality conditions. To address this, a framework integrating desktop and in situ UV–Vis spectrometers was developed to establish reliable conversion models. The absorption spectra obtained from a desktop spectrometer were utilized to create models for estimating nitrate-nitrogen (NO₃-N), total nitrogen (TN), chemical oxygen demand (COD), total phosphorus (TP), and suspended solids (SS). We validated these models using the absorption spectra obtained from an in situ spectrometer. Partial least squares regression (PLSR) employing selected wavelengths and principal component regression (PCR) employing all wavelengths demonstrated high accuracy in estimating NO₃-N and COD, respectively. The artificial neural network (ANN) was proved suitable for predicting TN in stream water with low NH₄-N concentration using all wavelengths. Due to the dominance of photo-responsive phosphorus species adsorbed onto suspended solids, PLSR and PCR methods utilizing all wavelengths effectively estimated TP and SS, respectively. The determination coefficients (R²) of all the calibrated models exceeded 0.6, and most of the normalized root mean square errors (NRMSEs) were within 0.4. Our approach shows excellent efficiency and potential in establishing reliable models monitoring nitrogen, phosphorus, COD, and SS simultaneously. This approach eliminates the need for time-consuming and uncertain in situ absorption spectrum measurements during model setup, which may be affected by fluctuating natural and anthropogenic environmental conditions.

The high-speed coolant pump facilitates thermal regulation in electric vehicle components, including batteries and motors, by circulating an ethylene glycol solution. This commonly used circulating fluid exhibits a notable negative correlation with temperature in terms of viscosity. Numerical simulations investigate the transient dynamics of a high-speed coolant pump operating at 6000 rpm, driving coolant flow at various temperatures. A high-speed coolant pump test rig is established, and the performance is evaluated under different temperature conditions. The numerical simulations at different temperatures align well with the experimental outcomes. Decreasing temperatures, from 100 to −20 °C, lead to reduced pump head and efficiency due to increased viscosity. Specifically, at a flow rate of 30 L/min, head decreases by 40.03% and efficiency by 44.19%. With escalating viscosity, the best efficiency point shifts toward lower flow rates. Notable impacts on both disk efficiency and hydraulic efficiency are observed due to viscosity fluctuations. It exerts minimal influence on volumetric efficiency at elevated flow rates but has a substantial impact on volumetric efficiency at lower flow rates. Increased fluid viscosity causes uneven pressure distribution within the pump, altering velocity profiles within the impeller. High-viscosity fluids tend to form large-scale vortex structures around the blades, reducing the thrust exerted by the blades on the fluid. Higher viscosity results in larger vortex structures around the blades, reducing thrust and increasing fluid frictional resistance. The study findings provide valuable insights for the advancement of high-efficiency, energy-saving, high-speed coolant pumps tailored for electric vehicles.