Coastal development such as sea reclamation, port terminals, and breakwater construction has significantly altered the southwestern coastline of Laizhou Bay, changing the regional hydrodynamic environment. To explore how tidal range and tidal prism respond to these 20-year coastline changes, this paper selected the southwestern coastline data of Laizhou Bay in 2000 and 2020, established a 2-D tidal model, and studied the impact of the 20-year coastline changes on tidal range and tidal prism in this sea area. The results show that during the 20 years from 2000 to 2020, most of the bay area’s coastline was in a growth trend of advancing toward the sea, 85.3% of the cross-sections were growth areas, 11.9% were dynamic stable areas, and there was almost no erosion area. Affected by reclamation activities, the area of the southwestern part of Laizhou Bay decreased by 11.66%, the coastline increased by 63.27%, and the center of mass moved 2.22 km to the northeast. The reclamation of tidal flats caused the potential energy of tidal waves in the bay to increase, the maximum possible tidal wave in the southwestern part of the bay showed an increasing trend, and the bay top between Weifang Port and Guangli Port increased particularly significantly, with a maximum increase of 22 cm. The spring tide period, neap tide period and average tidal prism in the southwestern bay decreased by 4.79%, 3.29%, and 4.24%, respectively. The reclamation of tidal flats is the main reason for the decrease in tidal prism in the southwestern part of the bay.

Meteorological factors are of crucial importance in foodborne disease (FBD) outbreaks, and more quantitative research is required to elucidate the underlying mechanisms. This study aimed to evaluate the impact of meteorological factors on FBD outbreaks in Yantai from 2014 to 2024, using data reported by the Yantai Foodborne Disease Surveillance Network. The distributed lag non-linear model (DLNM) was employed to model the exposure-response relationships between temperature, precipitation, wind speed, and FBD outbreaks. Prior to model fitting, multicollinearity among variables was addressed via the variance inflation factor (VIF). Results demonstrated non-linear relationships and complex correlations between these variables. Higher temperatures, greater precipitation, and slower wind speeds were associated with an increased risk of FBD outbreaks. In the lagged time dimension, temperature had an immediate effect that decayed after 2–3 weeks; the maximum lagged risk for precipitation occurred within 1–3 weeks; and wind speed showed subdued fluctuations. The lag patterns of high-value and low-value effects of different elements exhibited symmetry. These findings underline the necessity of integrating meteorological monitoring into FBD surveillance systems in accordance with their respective impact patterns.

Rapid urbanization drives significant land use transformations, making the timely detection of newly constructed buildings a critical research focus. This study presents a novel unsupervised framework that integrates pixel-level change detection with object-level, mono-temporal building information to identify new constructions. Within this framework, we propose the Building Line Index (BLI) to capture structural characteristics from building edges. The BLI is then combined with spectral, textural, and the Morphological Building Index (MBI) to extract buildings. The fusion weight (φ) between the BLI and MBI was determined through experimental analysis to optimize performance. Experimental results on a case study in Wuhan, China, demonstrate the method’s effectiveness, achieving a pixel accuracy of 0.974, an average category accuracy of 0.836, and an Intersection over Union (IoU) of 0.515 for new buildings. Critically, at the object-level—which better reflects practical utility—the method achieved high precision of 0.942, recall of 0.881, and an F1-score of 0.91. Comparative experiments show that our approach performs favorably against existing unsupervised methods. While the single-case study design suggests the need for further validation across diverse regions, the proposed strategy offers a robust and promising unsupervised pathway for the automatic monitoring of urban expansion.

Accurately understanding the impact of Significant Wave Height (SWH) on mariculture productivity is crucial for developing a sustainable blue economy and mitigating the effects of increasing marine extreme events under climate change. However, a significant research gap exists in macroscale empirical tools capable of quantifying the complex, non-linear, and spatially non-stationary relationships between SWH and mariculture yield. Addressing this, our study focused on the Bohai and Yellow Seas, a critical mariculture region in China. We developed five novel SWH indices (LSDI, MSDI, HSDI, RSI, NDSI) to statistically link SWH with the Unit Area Yield (UAY) using buoy-calibrated ERA5 reanalysis data and regional fishery statistics. Geographically Weighted Regression (GWR) was further employed to uncover the spatial heterogeneity of this relationship. Results demonstrated that the Normalized Difference SWH Index (NDSI) most effectively captured the SWH-UAY relationship (r = 0.61, R² = 0.37), as its non-linear form integrates the positive effects of low SWH conditions and the negative effects of high SWH conditions. GWR analysis revealed significant spatial non-stationarity, with the SWH impact on yield being stronger in the eastern and southern open waters of the Yellow Sea and weaker in the northern semi-enclosed Bohai Sea. The index framework and spatial analysis method developed in this study provide a transferable tool for quantifying the impact of physical oceanographic processes on mariculture productivity at a macro scale, which can offer a scientific basis for climate-resilient mariculture zoning and adaptive management.