Search Results (4)

Nutrients function as essential biological substrates for coastal phytoplankton growth and serve as pivotal indicators in marine environmental monitoring. The intensification of land-based nutrient sources inputs has exacerbated eutrophication in Chinese coastal water, while mechanistic understanding of differential self-purification processes among distinct land-based source nutrients (river source, domestic source, aquaculture source, and industrial source) remains limited, constraining accurate assessment of bay’s self-purification capacity. This study conducted incubation experiments in Tieshan Bay (TSB) during Summer (June 2023) and winter (January 2024), systematically analyzing the self-purification process of nutrients and associated environmental drivers. Distinct source-specific patterns emerged: river inputs exhibited maximal dissolved inorganic nitrogen (DIN) 1.390 ± 0.74 mg/L, whereas industrial discharges showed peak dissolved inorganic phosphorus (DIP) 4.88 ± 1.45 mg/L. Chlorophyll a (Chl-a) concentrations varied markedly across sources, ranging from 34.97 ± 23.37 μg/L (domestic source) to 86.63 ± 77.08 μg/L (river source). First-order kinetics demonstrated significant source differentiation (p < 0.05). River-derived DIN exhibited the highest attenuation coefficient (−0.3244 ± 0.17 d⁻¹), contrasting with industrial-sourced DIP showing maximum depletion (−0.4332 ± 0.20 d⁻¹). Correlation analysis indicated that summer was significantly associated with the impacts of three key control factors pH, dissolved oxygen, and turbidity on nutrient dynamics (p < 0.05), whereas winter exhibited a stronger dependence on salinity. These parameters collectively may modulate microbial degradation pathways and particulate matter adsorption capacities. These findings establish quantitative thresholds for coastal nutrient buffering mechanisms, highlighting the necessity for source-specific eutrophication mitigation frameworks. The differential self-purification efficiencies underscore the importance of calibrating pollution control strategies according to both anthropogenic discharge characteristics and regional hydrochemical resilience, which is of key importance for ensuring the traceability and control of land-based sources of pollution into the sea and the scientific utilization of the self-purification capacity of the bay water body. Full article

This study proposes a high-precision method for retrieving sea surface salinity (SSS) using GF-1 satellite imagery, focusing on Qinzhou Bay along the Guangxi coast. The analysis identified the spectral index B3×B4/(B1×B2) as having the strongest correlation with SSS (R = 0.929). To enhance the performance of the Back Propagation Neural Network (BPNN) model, optimization algorithms including Improved Grey Wolf Optimization (IGWO), Particle Swarm Optimization (PSO), and White Shark Optimization (WSO) were applied. Comparative results show that IGWO significantly optimized network weights and thresholds, yielding superior test performance metrics (MAE = 0.906 psu, MAPE = 4.124%, RMSE = 1.067 psu, and R² = 0.953), demonstrating strong generalization ability. Validation using third-party data indicated accuracy reductions of 10.9% and 8.6% in Qinzhou Bay and Tieshan Port, respectively, highlighting the model’s robustness and broad applicability. SSS retrieval results for Qinzhou Bay in 2023 revealed significant spatial and seasonal variations: the Inner Bay exhibited lower salinity (average 14 psu) from April to September due to freshwater inflows, while salinity increased (average 22 psu) from November to February. The Outer Bay, influenced by its connection to the South China Sea, maintained consistently high salinity levels (25–30 psu) year-round. Additionally, different models showed varying levels of effectiveness in Qinzhou Bay’s complex salinity environment; the IGWO–BPNN model, with its dynamic weight adjustment mechanism, demonstrated superior adaptability in areas with high salinity variability, outperforming other models. These findings suggest that the IGWO–BPNN model provides high accuracy and stability, supporting real-time, precise monitoring in Qinzhou Bay and similar coastal waters, thereby offering robust support for water quality management and marine conservation. Full article

Mangrove forests play a vital role in maintaining ecological balance in coastal regions. Accurately assessing changes in the ecosystem service value (ESV) of these mangrove forests requires more precise distribution data and an appropriate set of evaluation methods. In this study, we accurately mapped the spatial distribution data and patterns of mangrove forests in Guangxi province in 2016 and 2020, using 10 m spatial resolution Sentinel-2 imagery, and conducted a comprehensive evaluation of ESV provided by mangrove forests. The results showed that (1) from 2016 to 2020, mangrove forests in Guangxi demonstrated a positive development trend and were undergoing a process of recovery. The area of mangrove forests in Guangxi increased from 6245.15 ha in 2016 to 6750.01 ha in 2020, with a net increase of 504.81 ha, which was mainly concentrated in Lianzhou Bay, Tieshan Harbour, and Dandou Bay; (2) the ESV of mangrove forests was USD 363.78 million in 2016 and USD 390.74 million in 2020; (3) the value of fishery, soil conservation, wave absorption, and pollution purification comprises the largest proportions of the ESV of mangrove forests. This study provides valuable insights and information to enhance our understanding of the relationship between the spatial pattern of mangrove forests and their ecosystem service value. Full article

Land-source inputs into coastal water have increased remarkably in recent years, resulting in the deterioration of water quality, eutrophication, and algae blooms. However, we have limited understanding of spatiotemporal nutrient patterns, stoichiometry, and eutrophication assessment in Tieshan Bay coastal water at present. To investigate the rapid development of the coastal areas in Tieshan Bay in the South China Sea, nutrients and other physicochemical parameters were observed in Tieshan Bay during the normal season (April), wet season (July), and dry season (October) in 2021. The results showed that the average concentrations of dissolved inorganic nitrogen (DIN), dissolved inorganic phosphorus (DIP), and chemical oxygen demand (COD) in Tieshan Bay are 0.071 ± 0.115 mg/L, 0.008 ± 0.013 mg/L, and 0.71 ± 0.219 mg/L, respectively. DIN/DIP ratio ranges from 9.1–69.3, with an average value of 19.9 ± 19.2, which exceeds the Redfield value, behaving P limitations. In addition, the mean eutrophication index (EI) was low in Tieshan Bay, with an average value of 0.5 ± 1.5. Moreover, the hotspot coastal water with high DIN, DIP, and COD concentrations was located in the upper half of Tieshan Bay in all seasons. In addition to the DIN, DIP, and COD contributions to EI, the average contribution rates of DIN, DIP, and COD are 26.6%, 8.8%, and 64.6%, respectively, which leads to the largest contribution of COD to EI. Furthermore, the average comprehensive index (CI) of organic pollution in Tieshan Bay surface seawater ranged from −1 to 5.6. The seawater near Hepu in S8 station has organic pollution in wet and dry seasons, and Tieshan Bay’s middle region also has slight organic pollution. Additionally, the DIN, DIP, and COD had significant relationships with salinity (p < 0.05), suggesting that coastal water quality is affected by land-based sources input. To achieve the seawater quality target and mitigate regional eutrophication, it is critical to implement land-based source management across the river-bay-coastal water continuum. Full article