Ocean waves can spatiotemporally redistribute the momentum flux at the air‐sea interface, which varies with the sea state. Traditional atmosphere‐ocean coupled systems assume the ocean‐side stress (τ_oc) to be identical to the air‐side stress (τ_a); consequently, the role of ocean waves is neglected. In this study, the wave impacts on the air‐sea momentum flux are investigated based on 1‐year high‐resolution model simulations in the Baltic Sea using an atmosphere‐wave coupled model (Uppsala University‐Coupled Model, UU‐CM). The simulation results show that τ_oc can differ significantly from τ_a in both direction and magnitude. The direction difference between τ_oc and τ_a (DD(τ_oc, τ_a)) and the normalized momentum flux () decrease with increasing inverse wave age. In general, and DD(τ_oc, τ_a) are pronounced under wind‐following swell and wind‐crossing swell conditions, respectively. The occurrence frequencies of large and DD(τ_oc, τ_a) are higher nearer the coast; statistically, both decrease significantly with increasing water depth because of the joint effect of dissipation processes. Based on four selected areas, we find that alongshore winds (winds blowing parallel to the coastline) are favorable for large angular differences between τ_oc and τ_a (DD(τ_oc, τ_a) > 5°). However, onshore winds predominate at . The τ_a in the wave model is generally less than that obtained from the atmospheric model under low‐moderate wind conditions if the wave model feeds only the Charnock coefficient (roughness length) back to the atmospheric model in coupled systems.

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.

With the rapid acceleration of global urbanization, the impact of land use/cover change (LUCC) on the environment and ecosystems has become increasingly prominent, particularly in terms of air quality, which has emerged as a significant issue demanding attention. Focusing on the coastal city of Lianyungang, the spatiotemporal dynamics of land use/cover changes were explored by utilizing land use dynamic degree and land use transfer matrix methods. By integrating a comprehensive historical dataset, multiple linear regression analysis was used to analyze the driving mechanism of land use conversion and to explore the effect of LUCC on the variations in PM₁₀ levels. The results showed an overall decreasing trend in PM₁₀ levels over the 24-year period from 2000 to 2023, with distinct seasonal fluctuations, showing higher concentrations in winter and lower concentrations in summer. The impact of land use on PM₁₀ variations can be categorized into three stages: initial (2000–2006), transitional (2007–2013), and deepening development (2014–2022). Notably, during the third stage, with the involvement of policy interventions and industrial upgrading, a strong negative correlation of −0.97 was identified between urban land expansion and the decrease in PM₁₀ levels. The correlation between LUCC and PM₁₀ levels was insignificant over shorter periods, but the analysis of data from 2000 to 2022 revealed a significant positive correlation of 0.77, emphasizing the importance of adopting a long-term perspective to accurately assess the impact of LUCC on air quality. This research provides valuable insights into the implications of LUCC on air quality during urbanization and establishes a scientific foundation for developing air pollution management strategies in Lianyungang and similar regions.

Elevated duct is an atmospheric structure characterized by abnormal refractive index gradients, which can significantly affect the performance of radar, communication, and other systems by capturing a portion of electromagnetic waves. The South China Sea (SCS) is a high-incidence area for elevated duct, so conducting detection and forecasts of the elevated duct in the SCS holds important scientific significance and practical value. This paper attempts to utilize remote sensing techniques for extracting elevated duct information. Based on GPS sounding data, a lapse rate formula (LRF) model and an empirical formula (EF) model for the estimation of the cloud top height of Stratocumulus were obtained, and then remote sensing retrieval methods of elevated duct were established based on the Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data. The results of these two models were compared with results from the elevated duct remote sensing retrieval model developed by the United States Naval Postgraduate School. It is shown that the probability of elevated duct events was 79.1% when the presence of Stratocumulus identified using GPS sounding data, and the trapping layer bottom height of elevated duct well with the cloud top height of Stratocumulus, with a correlation coefficient of 0.79, a mean absolute error of 289 m, and a root mean square error of 598 m. Among the different retrieval models applied to MODIS satellite data, the LRF model emerged as the optimal remote sensing retrieval method for elevated duct in the SCS, showing a correlation coefficient of 0.51, a mean absolute error of 447 m, and a root mean square error of 658 m between the trapping layer bottom height and the cloud top height. Consequently, the encouraging validation results demonstrate that the LRF model proposed in this paper offers a novel method for diagnosing and calculating elevated ducts information over large-scale marine areas from remote sensing data.

The ecological impacts of expanding nearshore aquaculture demand accurate monitoring and a mechanistic understanding of underlying drivers. This study employed Landsat remote sensing images spanning 2000 to 2023 and a U-Net deep learning model to extract spatiotemporal patterns of laver aquaculture in Haizhou Bay, China, while also investigating the natural, technological, and socioeconomic factors influencing its growth. Key findings include: The U-Net model achieved an overall accuracy of approximately 98.9% and an F₁ score of around 0.887, significantly outperforming traditional classification methods (MLE, SVM, NN) by effectively reducing spectral confusion. The aquaculture area followed a “growth-peak-decline” pattern, peaking in 2018 at 10,872.45 hm², with a strong correlation to local government data. Among natural factors, only the 2-meter temperature showed a significant positive correlation with aquaculture expansion, while other factors like sea surface temperature and wind speed had minimal impact, suggesting that the region’s environmental stability supports large-scale production. Technological advancements, such as deep-sea farming and shellfish-algae intercropping, contributed to industry growth, while policy changes after 2019 resulted in a reduction of aquaculture area. Economic and policy interactions played a central role in spatial restructuring, with GDP positively correlating with aquaculture expansion during the growth phase (2000-2018), but negatively decoupling during the policy adjustment phase (2019-2023). This research provides a comprehensive framework for the sustainable management of coastal aquaculture by integrating remote sensing data with an analysis of multiple driving forces.