Search Results (126)

Marine protected areas (MPAs) are crucial for marine ecosystems, but they often pose significant challenges for the local fishing communities that rely on these ecosystems for their livelihoods. Identifying approaches that maintain ecological integrity while improving community livelihoods and well-being has become a central issue in marine sustainability. This study investigates the adaptive livelihood strategies of a community on Qi’ao Island, located in China’s Pearl River Estuary, which has gradually transitioned from traditional fisheries to tourism-induced livelihoods. Based on Actor–network theory (ANT), we developed a multi-level approach to examine interactions between human and non-human actors, institutions, and policies during livelihood adaptation. A mixed-methods approach was adopted, combining semi-structured interviews (n = 47), extended field observation, and policy analysis. Computational text analysis techniques included word frequency analysis, sentiment analysis, and co-occurrence network analysis using Python 3.8. These were integrated with thematic analysis and coding conducted in NVivo 15. This study demonstrates that the sustainability of tourism-based livelihood adaptation depends on equitable benefit sharing, flexible governance, and sustained community participation. Theoretically, this research extends livelihood studies by demonstrating how ANT captures the relational and processual dynamics of adaptation. Practically, it offers policy-relevant insights for designing adaptive and participatory governance strategies that reconcile conservation objectives with community well-being in MPAs. Full article

Based on an analysis of monitoring data from the Liaohe estuary, the distribution of inorganic nitrogen and active phosphate is related to the type of shoreline. The pollutant concentrations in the port area are 16% (inorganic nitrogen) and 59% (active phosphate) higher than those in the control area. The phytoplankton diversity index in the aquaculture area is 20% lower than in the mixed estuary area, which confirms the gradient effect of human disturbance. The constructed dual-mode distance effect model shows that, for a homogeneous shoreline, the goodness of fit is 40.1% in the non-estuary area, but radial basis function correction needs to be introduced for the estuary area. This study suggests that, in the port area, it is necessary to implement a combined policy consisting of ‘total nitrogen and phosphorus control + ecological compensation’, and artificial reefs should be built in the aquaculture area to maintain the number of benthic species. Full article

Monitoring trace metals in commercially important fish species provides an early warning of anthropogenic contamination and potential risk to consumers. This study semi-quantified and quantified essential, non-essential, and toxic elements in the muscle of wild meagre (Argyrosomus regius) captured in the Tagus estuary (Portugal), which is used as a nursery and spawning aggregation area. Dry muscle was microwave-digested and analyzed using inductively coupled plasma–optical emission spectroscopy. Semi-quantified screening detected Al, B, Ca, Fe, K, Mg, Na, P, S, Si, Sr, and Ti, and eight elements were determined using multielement calibration (As, Cr, Cu, Hg, Mn, Ni, Se, and Zn); Cd, Pb (toxic elements), Co, and Mo were not detected in this study. Arsenic was detected in all individuals, with a minimum value of 0.348 mg/kg wet weight. A mercury level above the European Commission regulatory limit (0.5 mg/kg wet weight) was only detected in one individual, corresponding to 2% of the samples. Although other metals remain well below regulatory limits, continued biomonitoring is recommended to track temporal trends and safeguard seafood safety in transitional coastal systems, which is important for commercially relevant fish species. Full article

This study presents the first comprehensive assessment of microalgal diversity in two Slovenian transitional waters (TWs): the shallow brackish lagoon of the Škocjanski Zatok Nature Reserve (SZNR) and the Rižana River estuary within the Port of Koper (PK) area. Between 2018 and 2021, water samples collected with a phytoplankton net were analyzed using light and scanning electron microscopy. In total, 240 species from 117 genera were identified in TW, dominated by diatoms and dinoflagellates, surpassing the diversity at a marine coastal station (91 species, 59 genera). Species richness was higher in PK (226) than in SZNR (154), mainly due to dinoflagellates and coccolithophores. Marine taxa predominated along the salinity gradient, with moderate contributions from brackish taxa and few freshwater forms, reflecting both natural and anthropogenic influences. Planktonic taxa dominated at all sites, while benthic forms were abundant in the lagoon, particularly in spring. Thirty-two taxa were recorded for the first time in Slovenian TW, mostly benthic or tychopelagic diatoms. The detection of Coolia monotis and five cyanobacterial genera with potentially harmful traits highlights the role of TW as an ecological interface. The taxonomic sufficiency analysis showed that the order level is sufficient to distinguish transitional from marine assemblages, beyond which ecological information is lost. Overall, this study highlights the importance of detailed taxonomic resolution for detecting microalgal diversity, including harmful and non-indigenous species to ensure robust ecological assessments under the WFD and MSFD directives. Full article

This study focuses on a rapidly urbanizing coastal plain where river networks serve as critical pathways for pollutant transport to nearshore waters. Under frequent sluice control and sluggish hydrodynamics, pollutants accumulate in channels and are subsequently flushed during intense rainfall or sluice-opening events, increasing pollutant loads in downstream estuaries. Based on 2017–2024 water quality monitoring data, integrated multi-source environmental factor analysis and unmanned patrol boat technology, systematic water quality assessment and pollution source identification were conducted. Significant spatial heterogeneity was observed: phosphorus and nitrogen pollution dominated in the eastern region, whereas the permanganate index was more prominent in the western part of the network. Identification of abrupt water quality change sections revealed industrial wastewater as the primary contributor to phosphorus and nitrogen, whereas permanganate index pollution originated widely from aquaculture, agriculture, and industrial discharges. Atmospheric deposition likely provides a non-negligible contribution to phosphorus and nitrogen input, with fluxes strongly correlated to rainfall. Sediment release posed internal risks of carbon and phosphorus, with intensity positively linked to pollution levels. This study elucidates the water quality characteristics and multi-source pollution mechanisms in typical coastal river networks under rapid economic development. Therefore, it provides a scientific basis for precise regional water environment management and coastal water quality protection. Full article

Rescue operations involving baleen whales trapped in dammed environments are difficult to perform successfully, yet increasingly relevant under growing coastal development. We report on a two-day (9–10 February 2023) rescue of a juvenile humpback whale trapped upstream of the Rance Tidal Power Station (TPS) in Brittany, France, providing rare peer-review evidence on response strategies in highly engineered estuaries. A collaborative, non-invasive strategy was implemented by adjusting water levels and creating artificial tidal currents to prevent the whale from stranding and to guide the individual back to open water. Approximately 100 people were mobilized as part of the rescue operation. This paper describes a detailed spatiotemporal account of the whale’s movements and a chronological record of the actions taken by the rescue team. After several attempts to guide it out, rescue efforts enabled its successful exit from the estuary on the second day of operations, and it was not subsequently reported stranded along the French coast. This case demonstrates the value of rapid multidisciplinary coordination between the French National Stranding Network (composed of marine scientists, veterinarians and local correspondents), local organizations, the local marine biology station, international marine mammal experts, national institutions, authorities and a tidal energy operator. Beyond documenting an unusual event, this paper provides operational lessons, highlighting (i) the adaptative management of a TPS as a guidance tool, (ii) the prioritization of animal welfare and responders’ safety, (iii) the effective public and media management and (iv) the involvement of trained volunteers during the rescue, promoting environmentally responsible behavior. These insights are transferable to other cases to inform future baleen whales rescue protocols in anthropogenic environments. Full article

Cryosat-2 SARin altimetric FBR data facilitates an opportunity to investigate phase differences between inland water radar reflections at the two antennae. With the antennae positioned cross-track, SARin was designed for the recovery of slope over ice margins, but here, it was used to recover off-pointing over inland waters. The ability to measure non-nadir off-pointing is verified using ocean data near the Amazon estuary to determine the satellite roll angle. Over inland waters, off-pointing requires correction to the nadir range and the geographic location of the reflectance. By using an SRTM-based water mask, the number of inland water reflectance increases significantly when off-pointing is considered. Comparisons between altimetric and river heights utilise gauge data at Tabatinga on the Solimões–Amazon. A least-squares adjustment yielded a river slope of −0.03506 ± 0.00003 m/km and a mean velocity of 1.803 ± 0.014 m/s over a river stretch of nearly 290 km. RMSE differences between the gauge and altimetry improve from 0.423 m to 0.404 m when off-pointing is taken into account for nadir inland water returns, showing the asymmetric effect of off-pointing. If all potential off-pointings are considered, the number of measurements increases by 66%, but the RMSE of 0.524 m is higher due to additional errors in the off-pointing corrections. Full article

The monitoring of turbidity in estuarine environments is a challenging essential task for managing water quality and ecosystem health. This study focuses on the lower reaches of the River Mersey, Liverpool. Harmonized Sentinel-2 MSI Level-2A imagery was integrated with in situ measurements from seven Environment Agency monitoring stations for two consecutive years (January 2023–January 2025). The workflow included image preprocessing, spectral index calculation, and the application of four machine learning algorithms: Gradient Boosting Regressor, XGBoost, Support Vector Regressor, and K-Nearest Neighbors. Among these, Gradient Boosting Regressor achieved the highest predictive accuracy (R² = 0.84; RMSE = 15.0 FTU), demonstrating the suitability of ensemble tree-based methods for capturing non-linear interactions between spectral indices and water quality parameters. Residual analysis revealed systematic errors linked to tidal cycles, depth variation, and salinity-driven stratification, underscoring the limitations of purely data-driven approaches. The novelty of this study lies in demonstrating the feasibility and proof-of-concept of using machine learning to derive spatially explicit turbidity estimates under data-limited estuarine conditions. These results open opportunities for future integration with Computational Fluid Dynamics models to enhance temporal forecasting and physical realism in estuarine monitoring systems. The proposed methodology contributes to sustainable coastal management, pollution monitoring, and climate resilience, while offering a transferable framework for other estuaries worldwide. Full article

Since the early 1990s, satellite altimetry has significantly improved our understanding of coastal and estuarine dynamics. The Casamance estuary in Senegal exemplifies a tropical microtidal system with limited instrumentation despite pressing environmental, social, and navigational concerns. This study explores the potential of SWOT satellite data to support the calibration and validation of high-resolution hydrodynamic models. Multi-source dataset of in situ measurements and altimetry observations has been combined with numerical modelling to investigate the hydrodynamics in response to physical drivers. Statistical metrics were used to quantify model performance. Results show that SWOT accurately captures water level variations in the main channel (width 800 m to 5 km), including both tidal and non-tidal contributions, with high correlation (R = 0.90) and low error (RMSE < 0.25 m). Performance decreases in tributaries (R = 0.42, RMSE up to 0.34 m), due to interpolated bathymetry and complex local dynamics. Notably, Delft3D achieves R = 0.877 at Diogué (RMSE = 0.204 m) and R = 0.843 at Carabane (RMSE = 0.225 m). These findings highlight the strategic value of SWOT for improving hydrodynamic modelling in data-scarce estuarine environments. Full article

Recognizing Other Effective Area-based Conservation Measures (OECMs) is a critical pathway for achieving the global “30 × 30” biodiversity target. China pioneered the Marine Ecological Redline (MERL) system to safeguard key marine ecosystems, rare and endangered species, and critical habitats through large-scale, legally mandated spatial regulation. However, MERLs have not yet been systematically assessed against OECM criteria. This study evaluates the institutional attributes and ecological effectiveness of MERLs, using the Pearl River Estuary as a case study, and identifies potential OECMs across non-MERL areas in China. The results show that MERLs fully meet OECM criteria, with the Pearl River Estuary MERLs demonstrating marked improvements in water quality, biodiversity recovery, and control of marine development intensity. We provide the first empirical evidence that MERLs function as a nationally led institutional OECM model, which enriches the typology of OECMs and introduces a novel governance pathway for marine biodiversity protection. Furthermore, eight types of non-MERL spatial units were identified as potential marine OECMs. By implementing policy and economic incentive mechanisms and establishing tiered recognition and dynamic identification systems, China can further biodiversity conservation and contribute to the global 30% marine protection goal. Full article

A methodology is proposed to predict the daily river discharge (RD) to estuaries from rivers draining in similar temperate zones. Multiple regression models are proposed to estimate RD using only available meteorological data. The models are based on monthly air temperature (T) and recent (P_R) and non-recent (P_NR) atmospheric precipitation (rainfall). They consist of the linear and nonlinear terms of T, P_R, and P_NR, without interaction terms between them. Four rivers located in the north and centre of Portugal (flowing to the Atlantic Ocean) are used in this study—Vouga, Antuã, Neiva, and Mondego. The optimal period used to compute the recent precipitation history is between 4 and 7 days for Vouga, Antuã, and Mondego and is 11 days for Neiva. The recommended lag to compute the non-recent precipitation history is between 50 and 90 days. The optimisation of the lengths of recent and non-recent periods improved the model performance, compared with previously proposed models with interaction terms between the meteorological variables. The obtained models provide a clear interpretation of the impact that meteorology has on RD. All rivers showed similar responses, but the flows of bigger rivers (Vouga, Mondego) were more significantly affected by precipitation and temperature. The proposed models are useful for analysing biogeochemical processes in rivers and estuaries, as well as for assessing flood and drought risks in sensitive areas. Full article

The remote sensing inversion of the Suspended Sediment Concentration (SSC) at the Yellow River estuary is crucial for regional sediment management and the advancement of monitoring techniques for highly turbid waters. Traditional in situ methods and low-resolution imagery are no longer sufficient for high-accuracy studies. Using SSC data from the Longmen Hydrological Station (2019–2020) and Sentinel-2 imagery, multiple models were compared, and the random forest regression model was selected for its superior performance. A non-parametric regression model was developed based on optimal band combinations to estimate the SSC in high-sediment rivers. Results show that the model achieved a high coefficient of determination (R² = 0.94) and met accuracy requirements considering the maximum SSC, MAPE, and RMSE. The B4, B7, B8A, and B9 bands are highly sensitive to high-concentration sediment rivers. SSC exhibited significant seasonal and spatial variation, peaking above 30,000 mg/L in summer (July–September) and dropping below 1000 mg/L in winter, with a positive correlation with discharge. Spatially, the SSC was higher in the gorge section than in the main channel during the flood season and higher near the banks than in the river center during the dry season. Overall, the random forest model outperformed traditional methods in SSC prediction for sediment-laden rivers. Full article

During the past decades, agricultural soil heavy metal pollution has been becoming increasingly severe due to urbanization and industrialization. However, the impact of externally input heavy metals on deep soils remains unclear because most previous relevant research only focused on surface soils. In the present study, Concentrations of eight heavy metals (Cu, Zn, Ni, Pb, Cr, Cd, As, and Hg) were determined for 72 pairs of surface and deep soil samples collected from an agricultural region close to the Pearl River estuary. Subsequently, heavy metal pollution and potential health risks were assessed using the Geo-accumulation Index and Potential Ecological Risk Index, a dose response model and Monte Carlo simulation, respectively. Principal component analysis (PCA) and the positive matrix factorization (PMF) receptor model were combined to analyze heavy metal sources. The results indicated that average concentrations of all heavy metals exceeded their corresponding background values. Cd was identified as the main pollutant due to its extremely high values of I_geo and Er. Unacceptable potential heavy metal non-carcinogenic and carcinogenic risks indicated by respectively calculated HI and TCR, higher than thresholds 1.0 and 1.0 × 10⁻⁴, mainly arose from heavy metals As, Cd, Cr, and Ni through food ingestion and dermal absorption. Anthropogenic sources respectively contributed 19.7% and 38.9% for soil As and accounted for the main contributions to Cd, Cu, and Hg (Surface: 90.2%, 65.4%, 67.3%; Deep: 53.8%, 54.6%, 56.2%) within surface and deep layers. These results indicate that soil heavy metal contents with deep layers were also significantly influenced by anthropogenic input. Therefore, we suggest that both surface and deep soils should be investigated simultaneously to gain relatively accurate results for soil heavy metal pollution and source apportionments. Full article

Monitoring chlorophyll-a concentration (Chl-a) is essential for assessing aquatic ecosystem health, yet its retrieval using remote sensing remains challenging in turbid coastal waters because of the intricate optical characteristics of these environments. Elevated levels of colored (chromophoric) dissolved organic matter (CDOM) and suspended sediments (aka total suspended solids, TSS) interfere with satellite-based Chl-a estimates, necessitating alternative approaches. One potential solution is machine learning, indirectly including non-Chl-a signals into the models. In this research, we develop machine learning models to predict Chl-a concentrations in the Chesapeake Bay, one of the largest estuaries on North America’s East Coast. Our approach leverages the Extra-Trees (ET) algorithm, a tree-based ensemble method that offers predictive accuracy comparable to that of other ensemble models, while significantly improving computational efficiency. Using the entire ocean color datasets acquired by the satellite sensors MODIS-Aqua (>20 years) and VIIRS-SNPP (>10 years), we generated long-term Chl-a estimates covering the entire Chesapeake Bay area. The models achieve a multiplicative absolute error of approximately 1.40, demonstrating reliable performance. The predicted spatiotemporal Chl-a patterns align with known ecological processes in the Chesapeake Bay, particularly those influenced by riverine inputs and seasonal variability. This research emphasizes the potential of machine learning to enhance satellite-based water quality monitoring in optically complex coastal waters, providing valuable insights for ecosystem management and conservation. Full article

Macroalgae are an integral part of estuarine primary production; however, their excessive growth may have severe negative impacts on the ecosystem. Although it is generally believed that algal blooms may be caused by a combination of excessive nutrients and temperature, their occurrences are hard to predict, and quantitative monitoring is a logistical challenge which requires the development of reliable and inexpensive techniques. This can be achieved by implementation of processing algorithms and indices on multi-spectral satellite images. Tuggerah Lakes estuary on the Central Coast of NSW was studied because of the regular occurrences of blooms, primarily of green filamentous algae. The detection of algal blooms based on the red-edge effect of the chlorophyll provided consistent results supported by direct observations. The Floating Algae Index (FAI) was identified as the most accurate index for detecting algal blooms in shallow areas, following a comparative analysis of six commonly used algae detection indices. Logistic regression was implemented where FAI was used as a predictor of two clusters, “bloom” and “non-bloom”. FAI was calculated for multi-spectral satellite images based on pixels of 20 × 20 m, covering the entire area of the Tuggerah Lakes. Seven sample points (pixels) were chosen, and the optimal threshold was found for each pixel to assign it to one of the two clusters. The logistic regression model was trained for each pixel; then the optimal parameters for its coefficients and the optimal classification threshold were obtained by cross-validation based on bootstrapping. Probabilities for classifying clusters as either “bloom” or “non-bloom” were predicted with respect to the optimal threshold. The resulting model can be used to estimate probability of macroalgal blooms in coastal estuaries, allowing quantitative monitoring through time and space. Full article