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.

Storm surge current-time estimations are strongly influenced by recent water-level conditions, while the temporal dependence among samples from the same typhoon event can complicate the assessment of model generalization to unseen events. This study evaluated storm surge estimation at the Lianyungang tide gauge using 2168 samples from 28 typhoon events during 2002–2024. A 41-feature extreme gradient boosting (XGBoost) model integrating historical surge and physics-motivated information was evaluated using fully nested event-grouped cross-validation, with complete outer-test typhoon events excluded from hyperparameter optimization, early stopping, and model fitting. The 41-feature XGBoost model achieved a mean absolute error (MAE) of 0.083 m, a root mean square error (RMSE) of 0.128 m, and a coefficient of determination (R²) of 0.794. On the common valid sample subset, its RMSE was 9.15% lower than that of Persistence, and it achieved a lower event-level RMSE in 18 of the 28 independently held-out typhoon events. Controlled information-source experiments showed that historical surge information accounted for most of the aggregate predictive skill, whereas adding core storm-state variables and the complete set of physics-motivated descriptors produced only limited changes in overall performance. The relative improvement over Persistence was larger for samples in which the shortest available historical surge lag was 3–6 h than for those with a 1 h lag, but this advantage did not extend consistently to the highest-surge conditions, where systematic underestimation remained evident. These results demonstrate the importance of event-independent validation for assessing machine-learning storm surge estimation and show that the model skill depends strongly on both information availability and the surge magnitude. The framework should be interpreted as a retrospective, single-station current-time estimation approach rather than an operational forecasting or extreme-surge warning system.

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.