Search Results (310)

A critical gap exists between coastal communities’ need for accessible flood risk assessment tools and the availability of sophisticated modeling, which remains limited by technical barriers and computational demands. This study introduces three key innovations through Coastal Defense Pro: (1) the first operational web-based AI ensemble for coastal flood risk assessment integrating real-time multi-agency data, (2) an automated regional calibration system that corrects systematic model biases through machine learning, and (3) browser-accessible implementation of research-grade modeling previously requiring specialized computational resources. The system combines Bayesian neural networks with optional LSTM and attention-based models, implementing automatic regional calibration and multi-source elevation consensus through a modular Python architecture. Real-time API integration achieves >99% system uptime with sub-3-second response times via intelligent caching. Validation against Hurricane Isabel (2003) demonstrates correction from 197% overprediction (6.92 m predicted vs. 2.33 m observed) to accurate prediction through automated identification of a Chesapeake Bay-specific reduction factor of 0.337. Comprehensive validation against 15 major storms (1992–2024) shows substantial improvement over standard methods (RMSE = 0.436 m vs. 2.267 m; R² = 0.934 vs. −0.786). Economic assessment using NACCS fragility curves demonstrates 12.7-year payback periods for flood protection investments. The open-source Streamlit implementation democratizes access to research-grade risk assessment, transforming months-long specialist analyses into immediate browser-based tools without compromising scientific rigor. Full article

Madagascar harbors a rich marine biodiversity, yet detailed knowledge of its fish species remains limited. Of the 1689 species listed in 2018, only 22% had accessible cytochrome oxidase I (COI) sequences in public databases. In response to growing pressure on fishery resources, this study aims to strengthen biodiversity monitoring tools. Its objectives were to enrich the COI database for Malagasy marine fishes, create the first 12S reference library, and evaluate the taxonomic resolution of different 12S metabarcodes for eDNA analysis, namely MiFish, Teleo1, AcMDB, Ac12S, and 12SF1/R1. An integrated approach combining morphological, molecular, and phylogenetic analyses was applied for specimen identification of fish captured using various types of fishing gear in Toliara and Ranobe Bays from 2018 to 2023. The Malagasy COI database now includes 2146 sequences grouped into 502 Barcode Index Numbers (BINs) from 82 families, with 14 BINs newly added to BOLD (The Barcode of Life Data Systems), and 133 cryptic species. The 12S library comprises 524 sequences representing 446 species from 78 families. Together, the genetic datasets cover 514 species from 84 families, with the most diverse being Labridae, Apogonidae, Gobiidae, Pomacentridae, and Carangidae. However, the two markers show variable taxonomic resolution: 67 species belonging to 35 families were represented solely in the COI dataset, while 10 species from nine families were identified exclusively in the 12S dataset. For 319 species with complete 12S gene sequences associated with COI BINs (Barcode Index Numbers), 12S primer sets were used to evaluate the taxonomic resolution of five 12S metabarcodes. The MiFish marker proved to be the most effective, with an optimal similarity threshold of 98.5%. This study represents a major step forward in documenting and monitoring Madagascar’s marine biodiversity and provides a valuable genetic reference for future environmental DNA (eDNA) applications. Full article

The focus of this study was on young populations of Kandelia obovata within a constructed coastal wetland in Haicang Bay, Xiamen, Southeast China. The objective was to systematically examine their allometric growth characteristics and carbon sequestration potential over an 8-year monitoring period (2016–2024). Allometric equations were developed to estimate biomass, and the spatiotemporal variation in both plant and soil carbon stocks was estimated. There was a significant increase in total biomass per tree, from 120 ± 17 g at initial planting to 4.37 ± 0.59 kg after 8 years (p < 0.001), with aboveground biomass accounting for the largest part (72.2% ± 7.3%). The power law equation with D²H as an independent variable yielded the highest predictive accuracy for total biomass (R² = 0.957). Vegetation carbon storage exhibited an annual growth rate of 4.2 ± 0.8 Mg C·ha⁻¹·yr⁻¹. In contrast, sediment carbon stocks did not show a significant increase throughout the experimental period, although long-term accumulation was observed. The restoration of mangroves in urban coastal constructed wetlands is an effective measure to sequester carbon, achieving a carbon accumulation rate of 21.8 Mg CO₂eq·ha⁻¹·yr⁻¹. This rate surpasses that of traditional restoration methods, underscoring the pivotal role of interventions in augmenting blue carbon sinks. This study provides essential parameters for allometric modeling and carbon accounting in urban mangrove afforestation strategies, facilitating optimized restoration management and low-carbon strategies. Full article

Coastal wetlands are critical ecosystems that require effective monitoring to support conservation and restoration efforts. This study evaluates the use of small unmanned aerial systems (sUAS) and multispectral imagery to estimate aboveground biomass (AGB) in tidal marshes, comparing models calibrated with destructive versus non-destructive in situ sampling methods. Imagery was collected over South Carolina’s North Inlet-Winyah Bay National Estuarine Research Reserve, and vegetation indices (VIs) were derived from sUAS imagery to model biomass. Stepwise linear regression was used to develop and validate models based on both sampling approaches. Destructive sampling models, particularly those using the Normalized Difference Vegetation Index (NDVI) and Difference Vegetation Index (DVI), achieved the lowest root mean square error (RMSE) values (as low as 70.91 g/m²), indicating higher predictive accuracy. Non-destructive models, while less accurate (minimum RMSE of 214.86 g/m²), demonstrated higher R² values (0.44 and 0.61), suggesting the potential for broader application with further refinement. These findings highlight the trade-offs between ecological impact and model performance, and support the viability of non-destructive methods for biomass estimation in sensitive wetland environments. Future work should explore machine learning approaches and improved temporal alignment of data collection to enhance model robustness. Full article

Dissolved oxygen (DO) is a fundamental water quality parameter that directly determines aquaculture productivity. China contributes 57% of the global aquaculture production, with the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) serving as a key contributor. However, this region faces significant environmental challenges due to increasing intensive stocking densities and outdated management practices, while also grappling with the systematic monitoring limitations of large-scale operations. To address these challenges, in this study, a random forest-based model was developed for DO concentration retrieval (R² = 0.82) using Landsat 8/9 OLI imagery. The Lindeman, Merenda, and Gold (LMG) algorithm was applied to field data collected from four cities—Foshan, Hong Kong, Huizhou, and Zhongshan—to identify key environmental drivers to the changes in DO concentration in these cities. This study also employed satellite imagery from multiple periods to analyze the spatiotemporal distribution and trends of DO concentrations over the past decade, aiming to enhance understanding of DO variability. The results indicate that the average DO concentration in fishponds across the GBA was 7.44 mg/L with a statistically insignificant upward trend. Spatially, the DO levels remained slightly lower than those in other waters. The primary environmental factor influencing DO variations was the pH levels, while the relationship between natural factors such as the temperature and DO concentration was significantly hidden by aquaculture management practices. The further analysis of fishpond water quality parameters across land uses revealed that fishponds with lower DO concentrations (7.293 mg/L) are often located in areas with intensive human intervention, particularly in highly urbanized regions. The approach proposed in this study provides an operational method for large-scale DO monitoring in aquaculture systems, enabling the qualification of anthropogenic influences on water quality dynamics. It also offers scalable solutions for the development of adaptive management strategies, thereby supporting the sustainable management of aquaculture environments. Full article

Northwestern Ontario has a shorter growing season but fertile soil, affordable land, opportunities for agricultural diversification, and a demand for canola production. Canola yield mainly varies with spatial heterogeneity of soil properties, crop parameters, and meteorological conditions; thus, existing yield estimation models must be revised before being adopted in Northwestern Ontario to ensure accuracy. Region-specific canola cultivation guidelines are essential. This study utilized high spatial-resolution images to estimate flower coverage and yield in experimental plots at the Lakehead University Agricultural Research Station, Thunder Bay, Canada. Spectral profiles were created for canola flowers and pods. During the peak flowering period, the reflectance of green and red bands was almost identical, allowing for the successful classification of yellow flower coverage using a recursive partitioning and regression tree algorithm. A notable decrease in reflectance in the RedEdge and NIR bands was observed during the transition from pod maturation to senescence, reflecting physiological changes. Canola yield was estimated using selected vegetation indices derived from images, the percent cover of flowers, and the M5P Model Tree algorithm. Field samples were used to calibrate and validate prediction models. The model’s prediction accuracy was high, with a correlation coefficient (r) of 0.78 and a mean squared error of 7.2 kg/ha compared to field samples. In conclusion, this study provided an important insight into canola growth using remote sensing. In the future, when modelling, it is recommended to consider other variables (soil nutrients and climate) that might affect crop development. Full article

Rapid suburbanization can alter catchment flow regime and increase stormwater runoff, posing threats to sensitive ecosystems. Applications of Nature-based Solutions (NbS) have increasingly been adopted as part of integrated water management efforts to tackle the hydrological impact of urbanization with co-benefits for improved urban resilience, sustainability, and community well-being. However, the implementation of NbS can be hindered by gaps in performance assessment. This paper introduces a physically based dynamic modeling approach to assess the performance of a nature-based storage facility designed to capture excess runoff from an urbanizing catchment (Armstrong Creek catchment) in Geelong, Australia. The study adopts a numerical modelling approach, supported by extensive field monitoring of water levels over a 2.5-year period. The model provides a decision support tool for Geelong local government in managing stormwater runoff to protect Lake Connewarre, a Ramsar-listed wetland under the Port Phillip Bay (Western Shoreline) and Bellarine Peninsula. Runoff is currently managed via a set of operating rules governing gate operations that prevents flows into the ecological sensitive downstream waterbody from December to April (drier periods in summer and most of autumn). Comparison with observed water level data at three monitoring stations for a continuous simulation period of May 2022 to October 2024 demonstrates satisfactory to excellent model performance (NSE: 0.55–0.79, R²: 0.80–0.89, ISE rating: excellent). Between 1670 × 10³ m³ and 2770 × 10³ m³ of runoff was intercepted by the nature-based storage facility, representing a 56–70% reduction in stormwater discharge into Lake Connewarre. Our model development underscores the importance of understanding and incorporating user interventions (gate operations and emergency pumping) from the standard operation plan to better manage catchment runoff. As revealed by the seasonal flow analysis for consecutive years, adaptive runoff management practices, capable of responding to rainfall variability, should be incorporated. Full article

The Bay of Bengal (BoB) is a global hub for marine elasmobranchs, particularly sharks and rays. These apex predators maintain and structure the balanced marine ecosystem and food webs. Marine elasmobranchs in Bangladesh are under-researched and under-managed, and face threats such as habitat degradation, global warming, pollution, illegal fishing, and overexploitation. This study aimed to evaluate the stock status of marine elasmobranches in the Bay of Bengal (BoB), Bangladesh. This research used catch and effort (CE) data for a period of 21 years (2002–2022). Both the Monte Carlo CMSY and BSM models were applied to assess biomass, exploitation rates, and sustainable yields. The BSM estimated a maximum carrying capacity (k) of 134,000 mt, which is larger than the CMSY estimate of 119,000 mt. The estimated intrinsic annual growth (r) from CMSY was 0.282. The MSY values ranged from 5110 mt (BSM) to 8420 mt (CMSY), with BSM indicating overexploitation, as the 2022 catch (7017 mt) exceeded the BSM-derived MSY. Both models suggested depleted and overfishing stock conditions, with B/B_MSY ratios < 1.0 and F/F_MSY ratios > 1.0. Effective management is crucial to prevent overfishing and ensure sustainable practices. Elasmobranch catches must be kept below the BSM-estimated maximum sustainable yield (MSY) of 5110 metric tons with fishing pressure maintained at or below F/F_MSY = 1.0. It is vital to regulate illegal and unlicensed fishing activities. Because of the aggregation of CE data, the results should be interpreted cautiously and never serve as a substitute for species-level assessments. Full article

Sea surface temperature (SST) is an important physical parameter that plays an important role in the study of various dynamic and thermodynamic processes in the ocean. Common SST retrieval methods are divided into single-channel methods (such as the single window algorithm) and multi-channel methods (such as the split window algorithm). To solve the problem of the low resolution of SST data used in coastal research, this study proposed a split window algorithm by adjusting the two important parameters, atmospheric transmittance and regression coefficients, to estimate SST using remotely sensed GF-5A images with a resolution of 100 m. The results were indirectly validated using MODIS temperature product and directly validated using measured data. The GF-5A image data obtained on 18 July 2024 were compared with MODIS data, giving R² of 0.985 and RMSE of 0.139 K. For the GF-5A image data obtained on 31 December 2024, the indirectly verified R² was 0.996 and the RMSE was 0.116 K. The R² and RMSE values of the direct verification of the accuracy of data from the two GF-5A images and the measured data were 0.999 and 0.613 K, respectively, which are better than the SST retrieval results of Landsat 8 data obtained at the same resolution. This work provides data support for subsequent research on the ecological environment and plant resources in the Bohai Bay. Full article

Marine litter threatens ocean ecosystems, and nautical tourism, as a source of litter, contributes significantly. This paper presents a qualitative and quantitative study of seafloor litter in the Bay of Selehovica in the northern Adriatic Sea. The bay is accessible only by sea and is attractive to nautical tourism vessels. The survey was conducted using a remotely operated vehicle across 22,100 m² of seafloor, before and after the tourist season (summer) in 2024. The analysis shows a 25.90% increase in litter items after one season. The predominant litter category is plastic, followed by glass, metal, rubber, and textiles. The abundance of marine litter increased from 1.3 to 1.7 items per 100 m² in the post-season, reflecting a measurable rise in litter density. Due to non-normal data distribution (Shapiro–Wilk test, p < 0.001), the Wilcoxon Signed-Rank Test was used, revealing a statistically significant increase in marine litter (W = 0, p < 0.001) with a large effect size (Cohen’s d = 0.89). A strong positive correlation between the pre- and post-season values was observed (Spearman’s r = 0.96, p < 0.001), suggesting that areas with higher initial litter levels tend to accumulate more over time. The results point to the necessity of targeted management strategies to reduce the pressure of nautical tourism on marine ecosystems and to protect the marine environment. Full article

This study examined a sediment plume from Australia’s largest river, The River Murray, which was produced during a major flood event in 2022–2023. This flood resulted from successive La Niña events, causing high rainfall across the Murray–Darling Basin and ultimately leading to a significant riverine flow through South Australia. The flood was characterised by a significant increase in riverine discharge rates, reaching a peak of 1305 m³/s through the Lower Lakes barrage system from November 2022 to February 2023. The water quality anomaly within the coastal region (<~150 km offshore) was effectively quantified and mapped utilising the diffuse attenuation coefficient at 490 nm (Kd490) from products derived from MODIS Aqua Ocean Color satellite imagery. The sediment plume expanded and intensified alongside the increased riverine discharge rates, which reached a maximum spatial extent of 13,681 km². The plume typically pooled near the river’s mouth within the northern corner of Long Bay, before migrating persistently westward around the Fleurieu Peninsula through Backstairs Passage into Gulf St Vincent, occasionally exhibiting brief eastward migration periods. The plume gradually subsided by late March 2023, several weeks after riverine discharge rates returned to pre-flood levels, indicating a lag in attenuation. The assessment of the relationship and accuracy between the Kd490 product and the surface-most in situ turbidity, measured using conductivity, temperature, and depth (CTD) casts, revealed a robust positive linear correlation (R² = 0.85) during a period of high riverine discharge, despite temporal and spatial discrepancies between the two datasets. The riverine discharge emerged as an important factor controlling the spatial extent and intensities of the surface sediment plume, while surface winds also exerted an influence, particularly during higher wind velocity events, as part of a broader interplay with other drivers. Full article

To investigate the spatiotemporal distribution and causes of blooms of Rhopilema esculentum in Hangzhou Bay during 2024, this study investigated its growth characteristics, including umbrella diameter and body weight, along with environmental factors, spatiotemporal dynamics and yield variations. The analysis was based on the 2024 monitoring data of R. esculentum resources in Hangzhou Bay, together with relevant social research data. The results showed that umbrella diameter and body weight increased over time at all monitoring points. The growth rate of the R. esculentum umbrella diameter declined gradually over time, whereas that of body weight rapidly increased. The daily growth rate of umbrella diameter in the water of Tangnao and Xiaoji Mountains was significantly higher than that in the waters of Tanxu Mountain. A sharp drop in salinity caused by Xin’anjiang Reservoir flood discharge from the 23rd to 28th June was the primary cause of the R. esculentum blooms in Hangzhou Bay. During the special R. esculentum fishing period in the summer fishing moratorium, R. esculentum was mainly distributed in the southern and eastern Hangzhou waters, with a maximum daily yield of 28,000 kg/day. After the 16th, R. esculentum production expanded across the entire bay, with blooms also occurring in Xiangshan Bay and Liuheng, reaching a production peak of 44,000 kg/day. In 2024, R. esculentum production in Hangzhou Bay totalled 250,000 tonnes, breaking historical records. This study revealed the 2024 growth and spatiotemporal dynamics of R. esculentum in Hangzhou Bay, providing a reference for the rational use and protection of the species and revealing the causes of the unprecedented blooms. Full article

Understanding the correlation between PM_2.5 and O₃ is critical for complex air pollution control. This study comprehensively analyzed PM_2.5 and O₃ pollution characteristics, uncovered spatiotemporal variations in their correlation, and investigated the driving mechanisms of their association in Dongying, a typical petrochemical city in China’s Bohai Bay region. Results showed that PM_2.5–O₃ correlation in Dongying exhibited significant seasonal variations, spatial patterns, and concentration threshold effects from 2017 to 2023. PM_2.5 and O₃ showed strong positive correlations in summer, negative in winter, and weak positive in spring/autumn, with strongest links in western areas. The strongest positive PM_2.5–O₃ correlation occurred in summer when PM_2.5 ≤ 35 μg·m⁻³ and O₃ >160 μg·m⁻³, while the strongest negative correlation was exhibited in winter with PM_2.5 > 75 μg·m⁻³ and O₃ ≤ 100 μg·m⁻³. Meteorological conditions (T > 20 °C, RH < 30%, wind speed < 1.73 m/s, O_x > 125 μg·m⁻³) and non-sea-breeze periods enhanced the PM_2.5–O₃ positive correlation. During the four typical pollution episodes, the positive PM_2.5–O₃ correlation in summer was propelled by synchronous increases in O₃ and secondary components via shared precursors. In autumn, strong positivity resulted from secondary component–O₃ correlations (r > 0.7) and dominance of secondary formation in PM_2.5. In winter, the negative correlation stemmed from primary emissions inhibiting photochemistry. Random forest analysis showed that O_x, RH, and T drove positive PM_2.5–O₃ correlation via photochemistry in summer, whereas winter primary emissions and NO titration caused negative correlation. This study offers guidance for the collaborative PM_2.5 and O₃ control in the petrochemical cities of the Bay region. Full article

Background: Bai Hu Tang (BHT) is a classic antipyretic in traditional Chinese medicine, however, there is little scientific evidence on the mechanism and material basis of its antipyretic effect. Methods: In LPS-induced febrile rats, after administration of BHT at 42 g/kg for half an hour, body temperature was measured at hourly intervals for 9 consecutive hours. Then, serum levels of TNF-α, IL-1β, and IL-6, and serum and cerebrospinal fluid (CSF) levels of AVP, cAMP, PGE2, Ca and CRH, and the remaining sera were used for metabolomics. These were then combined with network pharmacology methodology to further analyse the antipyretic effect of BHT and then dock key targets with differential components. Results: Administration of BHT to LPS-induced febrile rats significantly reduced elevated body temperature, TNF-α, IL-1β and IL-6 levels, but serum and CSF levels of AVP, cAMP, PGE2, Ca²⁺ and CRH were significantly elevated compared to the control group. Network pharmacological analyses indicated that the putative functional targets of BHT were regulation of immune responses, associated protein binding and inflammatory responses, and fine-tuning of phosphatase binding and activation of signalling pathways such as MAPK, PI3K, AKT, NF-kB, cAMP and inflammatory pathways. Metabolomic analysis showed that the antipyretic effect of BHT and its mechanism are likely to be involved in fatty acid metabolism, bile acid metabolism and amino acid metabolism in the organism, with L-arginine, glycyrrhetinic acid and N-acetylpentraxine as the main differential metabolites that play a significant role in heat recovery. The results also showed better docking of glycyrrhetinic acid with TNF-α, IL-6R, PTGS2. Conclusions: BHT provides a valuable adjunct to traditional clinical antipyretics by improving body temperature and metabolism and reducing inflammation. Full article

Understanding spatiotemporal patterns of fish density and their environmental drivers is critical for managing river–lake ecosystems, yet dynamic interactions in heterogeneous habitats remain poorly quantified. This study combined hydroacoustic surveys, spatial autocorrelation analysis (Moran’s I), and generalized additive models (GAMs) to investigate seasonal and spatial fish distribution, aggregation characteristics, and regulatory mechanisms in China’s Zhelin Reservoir. The results reveal pronounced seasonal fluctuations, with summer fish density peaking at 13.70 ± 0.91 ind./1000 m³ and declining to 1.95 ± 0.13 ind./1000 m³ in winter. Spatial heterogeneity was evident, with the Xiuhe region sustaining the highest density (15.69 ± 1.09 ind./1000 m³) and persistent hotspots in upstream bays. Transient high-density clusters (90–99% confidence) near the Zhelin Dam during summer suggested thermal or hydrodynamic disturbances. GAM analysis (R²adj = 0.712, 78.5% deviance explained) identified seasonal transitions (12.26% variance), water depth (16.54%), conductivity (13.75%), and dissolved oxygen (13.29%) as dominant drivers, with nonlinear responses to depth and bimodal patterns for conductivity/oxygen. These findings demonstrate that hydrological seasonality and habitat heterogeneity jointly govern fish aggregation, underscoring the ecological priority of Xiuhe and upstream bays as core habitats. This study provides a mechanistic framework for guiding reservoir management, including targeted conservation, dam operation adjustments to mitigate hydrodynamic impacts, and integrated strategies for balancing hydrological and ecological needs in similar ecosystems. Full article