Semi-enclosed bays are greatly affected by human activities and have undergone drastic changes in their ecological environment, which requires our continuous attention. Laizhou Bay (LZB) is a typical semi-closed bay that is greatly influenced by human activities, and it is highly representative on a global scale. Investigating the multi-scale variation in nutrient concentrations in semi-enclosed bays can provide scientific support for environmental management and policy adjustments. To address the limitations of in situ data and the high cost of field surveys, this study utilizes machine learning methods to construct MODIS remote sensing models for quantitatively analyzing the concentrations of dissolved inorganic nitrogen (DIN) and dissolved inorganic phosphorus (DIP) in the surface water of LZB, as well as the spatiotemporal factors influencing them. Among various methods tested, the Support Vector Machine Regression (SVR) algorithm demonstrated the best performance in retrieving nutrient concentrations in LZB. The R² values of the DIN and DIP retrieval results based on the SVR algorithm are 0.91 and 0.92, respectively, while the RMSE values are 5.43 and 0.08 μmol/L, respectively. The retrieval results indicate that nearshore nutrient concentrations are significantly higher than those in offshore areas. Temporally, from 2003 to 2024, the DIN concentration in l has decreased at a rate of 0.4 μmol/L/yr, while the DIP concentration has remained relatively stable. Given sufficient observation data, the proposed machine learning and remote sensing approach can be effectively applied to other bays, offering the advantages of long time series, high spatial resolution, and a low cost.

A promising strategy to mitigate Carbon dioxide emissions involves the addition of finely ground alkaline minerals to activated sludge systems. However, the release of bioactive substances from these minerals alters the microenvironment of the sludge, with the potential to induce shifts in the bacterial community. In this study, the responses of the bacterial communities in an aerobic activated sludge system to two alkaline minerals (olivine and steel slag) were investigated. This study demonstrated that the addition of olivine and steel slag to activated sludge could selectively shape the microbial community structure. The results indicated a higher diversity of the attached bacterial community in the olivine and steel slag group compared to the glass group. Olivine significantly enriched the bacterial genera that were involved in organic matter degradation and denitrification, such as SC-I-84 and Candidatus Competibacter, thereby potentially enhancing the system’s efficiency in removing carbon and nitrogen pollutants, while the steel slag promoted the growth of iron-dependent denitrifying phosphorus-accumulating bacteria including Thermomonas and Arenimonas, thus establishing a microbial consortium with the potential for nitrogen and phosphorus removal in activated sludge systems. These findings provide crucial evidence for developing mineral–microbe synergistic strategies toward carbon capture and low-carbon sewage treatment.