A new method was presented to prepare hydrophilic PES/SPSF flat-sheet membrane by a reverse thermally induced phase separation (RTIPS) method to enhance permeability and hydrophilicity. SPSF was self-made and was blended to improve the hydrophilicity of PES flat-sheet membrane. The performance of PES/SPSF flat-sheet membrane, which varied with SPSF content and coagulation water bath temperature, was investigated by SEM, FTIR, AFM, pure water flux, BSA rejection rate, water contact angle and long-term testing. FTIR results proved the successful blending of SPSF with PES membrane, SEM images showed that dense skin surface and finger-like structure emerged in the membrane fabricated by NIPS method, while a porous top surface and sponge-like structure emerged in the membrane fabricated by RTIPS. The pure water flux and BSA rejection rate of the membrane for RTIPS were both higher than those for NIPS. AFM images revealed that surface roughness increased with the addition of SPSF. The water contact angle decreased with the increase of SPSF, which illustrated better hydrophilicity with the addition of SPSF. The flat-sheet PES membrane prepared with 2 wt% SPSF by RTIPS method exhibited decent properties, reaching maximum pure water flux (966 L m⁻² h⁻¹) and at the same time the BSA rejection rate was 79.2%. The long-term test proved that the anti-fouling performance of PES/SPSF membrane was better than that of PES membrane.

The distribution of the surface suspended sand concentration (SSSC) in the Yangtze River estuary is extremely complex. Therefore, effective methods are needed to improve the efficiency and accuracy of SSSC inversion. Hyperspectral remote sensing technology provides an effective technical means of accurately monitoring and quantitatively inverting SSSC. In this study, a new framework for the accurate inversion of the SSSC in the Yangtze River estuary using hyperspectral remote sensing is proposed. First, we quantitatively simulated water bodies with different SSSCs using sediment samples from the Yangtze River estuary, and analyzed the spectral characteristics of water bodies with different SSSCs. On this basis, we compared six spectral transformation forms, and selected the first derivative (FD) transformation as the optimal spectral transformation form. Subsequently, we compared two feature band extraction methods: the successive projections algorithm (SPA) and the competitive adaptive reweighted sampling (CARS) method. Then, the partial least squares regression (PLSR) model and back propagation (BP) neural network model were constructed. The BP neural network model was determined as the best inversion model. The new FD-CARS-BP framework was applied to the airborne hyperspectral data of the Yangtze estuary, with R² of 0.9203, RPD of 4.5697, RMSE of 0.0339 kg/m³, and RMSE% of 8.55%, which are markedly higher than those of other framework combination forms, further verifying the effectiveness of the FD-CARS-BP framework in the quantitative inversion process of SSSC in the Yangtze estuary.