Phaeocystis globosa (P. globosa) is a unique causative species of harmful algal blooms, which can form gelatinous colonies. We, for the first time, used unmanned aerial vehicle (UAV) measurements to identify P. globosa blooms and to quantify the biomass. Based on in situ measured remote sensing reflectance ( ${R_{\mathrm{ rs}}}$ ), it is found that, for P. globosa blooms, the maximum of the second-derivative ( ${d\lambda ^{2}{R}_{\mathrm{ rs}}}$ ) of ${R_{\mathrm{ rs}}(\lambda)}$ in the 460–480-nm domain is beyond 466 nm. An analysis of the absorption properties from algal cultures suggested that this feature comes from the absorption of chlorophyll ${c_{3}}$ (Chl $-/{c_{3}}$ ) around 466 nm, a prominent feature of P. globosa. This position of $ {d\lambda ^{2}{R}_{\mathrm{ rs}}}$ maximum was, thus, selected as the criterion for P. globosa identification. The spatial extent of P. globosa blooms in two bays off southern China was then mapped by applying the criterion to UAV-measured ${R_{\mathrm{ rs}}}$ . Twelve out of 16 UAV and in situ match-up stations were consistently identified as dominated by P. globosa, indicating the accuracy of 75%. Furthermore, using localized empirical models, chlorophyll a (Chl $-/{a}$ ) concentration and colony numbers of P. globosa were estimated from UAV-derived ${R_{\mathrm{ rs}}}$ , where P. globosa colonies were found in a range of ~3–37 gel matrix/L, indicating the occurrence of weak to moderate P. globosa blooms during the surveys. The promising results suggest a high potential for detection and quantification of P. globosa blooms in near-shore bays or harbors using UAV-based hyperspectral remote sensing, where conventional ocean color satellite remote sensing runs into difficulties.

The Yellow Sea Cold Water Mass (YSCWM) is an important component of the hydrodynamic system and it significantly impacts the primary production of the Yellow Sea. This study investigated the difference in the interannual variability and long-term trends between the northern YSCWM (NYSCWM) and southern YSCWM (SYSCWM), and explored the main physical environmental factors that led to their inconsistency using multiple wavelet coherence. On the interannual scale, the intensities of the NYSCWM and SYSCWM exhibited consistent variability, but the intensity of the SYSCWM had a larger standard deviation and longer periodic signal than that of the NYSCWM. The two-factor combination of surface air temperature (SAT)–Niño 3.4 in the NYSCWM and sea surface temperature (SST)–northward seawater velocity (Vgos) in the SYSCWM controlled the interannual variability, which meant the influencing intensity variability differed in the NYSCWM and SYSCWM. In the long-term trend, the intensities of the NYSCWM and SYSCWM both showed decreasing trends during the study period. However, the enhanced circulation provided more horizontal heat input into the SYSCWM, and the relatively higher increase in SST and decrease in the amplitude of variation in the thermocline depth promoted vertical heat exchange in the SYSCWM, thereby making the intensity of the SYSCWM decrease more quickly than that of the NYSCWM. These findings provide important references that facilitate a deeper understanding of the influence of hydrological processes on marine ecosystems in marginal seas.

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.