River-ocean continuums are rich environments key to the transformations of organic matter and nutrients from many sources. Human impacts on these ecosystems can be local, upstream, or global. Particulate organic matter provides one tool for exploring these processes; inventories and carbon and nitrogen stable isotopes are useful indicators. This study conducted comprehensive field investigations from the upper reaches of the Yangtze River to the coastal waters to explore the distribution and spatial-temporal variation of particulate organic carbon (POC) and nitrogen (PON), and dissolved organic carbon (DOC) in surface waters from April–May (Spring) and October–November (Autumn) 2014. Carbon and nitrogen isotopes (δ¹³C and δ¹⁵ N) were used to assess organic matter sources. Numerous environmental factors were also measured. PON and POC exhibited similar spatial and temporal variations along the river mainstream. POC increased from the upper to lower reaches, similarly in both seasons; POC declined seaward from the river mouth. In contrast, PON showed greater seasonal and spatial variations along the continuum, with greater primary production in offshore areas. DOC along the continuum was higher in autumn, likely related to greater river discharge and warmer waters. The DOC/POC ratios by weight were >1 in most samples except for the ones collected from the turbidity maximum zone, suggesting that DOC contributed to a larger fraction of the total organic matter inventories. End-member mixing models revealed that the proportion of organic matter from upstream (allochthonous) sources was >70% in the mainstream during both seasons. In coastal waters, the proportion of allochthonous sources was only >57%. Compared with other major world rivers, DOC and allochthonous POC are especially high in the Yangtze River-ocean continuum. Human activities causing variations in terrestrial inputs are likely an important driving factor. In addition, the contribution of soil erosion caused by urbanization to riverine organic carbon cannot be ignored.

Our study aims to investigate the water quality and distribution patterns of phytoplankton communities in the Jinjiang River Basin in Quanzhou, as well as their relationship with environmental factors. We integrated data from the national water quality databases of the two main tributaries of the West and East Jinjiang Rivers between 2020 and 2023, supplemented by field surveys. Redundancy analysis was used to explore the effect of environmental factors on phytoplankton communities. Our findings revealed that the West Jinjiang River experienced a significant influence from excessive fertilizer use in tea cultivation, leading to an increase in TN concentrations compared to the East Jinjiang River. The abundance of phytoplankton in the Jinjiang River Basin was 10⁵ cells·L⁻¹, with phytoplankton being dominated by Chlorophyta, Cyanphyta, and diatoms, accounting for an average of 50%, 20%, and 19% of the total phytoplankton abundance, respectively. Redundancy analysis indicated that temperature, pH, and nutrient concentrations were important factors influencing the phytoplankton communities. With increasing temperature and nutrients concentrations, the abundance of Chlorophyta and Dinophyta significantly increased. This study provides a solid foundation for the regular “health diagnosis” of crucial rivers and lakes in Quanzhou and supports the establishment of a health guarantee system for rivers and lakes.

The coupling of physical transport and phase-transfer processes represents a fundamental mechanism governing metal cycling in estuarine systems under tidal oscillations. Taking Quanzhou Bay as a model system, we conducted continuous observations and sample collection at the river channel (Q1), the turbidity maximum zone (Q2), and the outer bay channel (Q3). The metals (Al, Ti, Ba, Cu, Mn, and Zn) were measured by ICP-MS to systematically investigate the distribution, transport, and inter-media transfer across multiple water layers under varying estuarine processes. Our findings demonstrate that particulate metal concentrations in Quanzhou Bay exhibit strong synchrony with suspended sediment concentrations (SSC) over tidal cycles, displaying a distinct sediment-following pattern controlled by alternating end members. Particulate metal fluxes during flood and ebb-tides generally followed the hierarchy Q1 > Q2 >> Q3. Notably, stations Q1 and Q2 were dominated by flood-tide fluxes with net transport directed landward, whereas Q3 was characterized by ebb tide dominance with net flux directed seaward—revealing a spatial division of labor between “inner bay retention/reallocation” and “outer bay channel export”. In contrast, dissolved metals exhibited marked element-specific responses to tidal forcing: Al and Ti increased during flood tides at stations Q1 and Q2, while Ba and Cu showed opposite trends, and Mn and Zn displayed more conservative behavior. Concurrently, solid/liquid partition coefficient (logKd) values for Al, Ti and Ba, Cu exhibited inverse patterns over tidal cycles, suggesting divergent adsorption–desorption regulation under identical hydrodynamic conditions that drives differential phase-transfer dynamics. These disparities likely reflect intrinsic chemical properties and source variations among the elements. This study elucidates, at the tidal timescale, the coupled processes of “alternating end-member control—estuarine filter modulation—concurrent channelized export and inner bay retention” in Quanzhou Bay, providing critical process-level insights for metal flux quantification and bay pollution remediation initiatives in an ecological restoration project.

Algal proliferation in subtropical drinking water reservoirs has become increasingly severe, and developing a reliable prediction for algal abundance through high-frequency in situ data is essential for early risk warning and effective management. This study analyzed the interannual variations in algal abundance in the Shanmei (SM) Reservoir, located in Quanzhou City, Fujian Province, China, based on the high-frequency data between 2020 and 2025, and forecasted algal abundance 24 h ahead via the optimized Transformer model. Results revealed that the SM reservoir exhibited seasonal variability in environmental factors, with persistently elevated pH during spring and summer, ranging from 7.12 to 9.66, and relatively high total nitrogen concentrations, ranging from 1.17 to 2.28 mg/L. Overall, algal abundance increased throughout the study period, and the annual average algal abundance in 2025 was 8.18 × 10⁶ cells/L, which was twice that in 2021. Model comparisons revealed that the optimized Transformer model exhibited the highest performance in terms of R² = 0.88 when predicting the next hour using 12 days of data. Feature importance analysis based on SHapley Additive exPlanations (SHAPs) revealed that the predictions of algal dynamics were primarily influenced by previous-hours algal abundance, permanganate index, dissolved oxygen, air temperature, wind speed, and pH. This study revealed that the optimized independent learning model with integrated multi-scale features can significantly enhance the predictive performance of algal dynamics, offering a technical basis for early warning of algal blooms and refined reservoir management.