The rapid industrialization and urbanization in China have exacerbated air pollution, particularly PM_2.5, posing significant threats to public health. This study focused on Lianyungang, an industrial city, to analyze the spatiotemporal variations in PM_2.5 concentrations from 2000 to 2023 and identify the influencing factors. Utilizing high-resolution PM_2.5 data from the ChinaHighPM_2.5 dataset and ERA5 meteorological data, the study employed Empirical Orthogonal Function (EOF) analysis to capture spatial variability and the Bayesian Estimator of Abrupt Change Seasonal and Trend (BEAST) to assess long-term trends and abrupt changes. The key findings include a marked seasonal pattern, with higher PM_2.5 levels during the winter months and lower concentrations in the summer, primarily driven by temperature, humidity, and precipitation. A significant decline in PM_2.5 levels was observed after 2014, following the implementation of pollution control measures. The study underscores the importance of continued environmental regulation and green technology adoption in mitigating air pollution in rapidly industrializing cities. This research provides a comprehensive analysis of PM_2.5 trends and highlights the critical role of natural and human factors, contributing valuable insights for policymakers and researchers aiming to improve air quality.

Driven by climate change and population growth, coastal flood risk is rising, making high-precision Digital Elevation Models (DEMs) essential for inundation simulation and risk assessment. Although global open-source DEMs are increasingly available, their regional applicability and uncertainty still require quantitative evaluation. Taking Lianyungang, a coastal city in eastern China, as the study area, this study used ICESat-2 ATL08 laser altimetry as the reference to assess the vertical accuracy of eight mainstream open-source DEMs: the ASTER GDEM, FABDEM, AW3D30 DEM, SRTM DEM, MERIT DEM, NASA DEM, Copernicus DEM, and TanDEM-X DEM. The effects of slope, aspect, and land cover on DEM errors were analyzed, and the Height Above Nearest Drainage (HAND) model was used to evaluate how DEM vertical accuracy and spatial resolution affect flood inundation simulation. The results show that the FABDEM has the highest accuracy (RMSE = 1.24 m; NMAD = 0.49 m), followed by the Copernicus DEM GLO-30 (RMSE = 1.56 m; NMAD = 0.65 m), whereas the ASTER GDEM performs worst (RMSE = 5.36 m; NMAD = 3.69 m). The SRTM DEM systematically underestimates ICESat-2 elevations, with mean and median errors of −1.85 m and −1.80 m, mainly due to acquisition time differences and land-use changes in Lianyungang. DEM errors generally increase with slope, are higher on west-facing slopes, and are larger over water bodies than over cropland and impervious surfaces. HAND simulations show that DEM-derived inundation differences are greatest under low-threshold conditions. At the 1 m HAND threshold, the MERIT DEM produces the largest inundation area (4370.28 km²), while the ASTER GDEM produces the smallest area (3330.53 km²); these differences decrease as the threshold increases. Overall, the FABDEM provides the most accurate flood inundation representation in Lianyungang, while the Copernicus DEM GLO-30 is a reliable alternative.