The eastern coast of India is one of the regions where most of the population resides in urban areas in the low-elevation coastal zone, making it vulnerable to frequent extreme weather events. The objectives of this study are to assess the short- to long-term shoreline changes of the Odisha coast, to understand how anthropogenic influences, and particularly extreme natural events, affect these changes, and to predict shoreline changes for 2050. This study utilized multi-temporal/spectral/spatial resolution satellite images and a digital shoreline analysis (DSAS) tool to appraise the short- (at five/six-year intervals) and long-term (1990-2019) shoreline dynamics along the coastal part of Odisha over the past three decades (1990-2019). The long-term shoreline analysis shows that the mean shoreline change is about 0.67 m/year and highlights that 52.47 % (227.4 km), 34.70 % (150.4 km), and 12.83 % (55.6 km) of the total Odisha coastline exhibit erosion, accretion, and stability, respectively. During the short-term analysis, the 2000-2005 period had the highest percentage of erosion (64.27 %), followed by the 2005-2010 period with an erosional trend of 59.06 %. The 1995-2000 period showed an accretion trend, whereas, during the last period, i.e., 2015-2019, the percentage of transects depicting erosion and accretion was almost similar. In 2050, 55.85 % of the transects are expected to show accretion, while 44.15 % would show erosion or a constant trend. The study identified the hotspots of coastal erosion along delineated study zones by synthesizing data from previous studies as well. The regional analysis of shoreline change along the Odisha coast would not only provide coastal managers with critical information on shoreline dynamics but also draw attention to vulnerable areas linked to shoreline dynamicity along the coast.
Meghna River Estuary, the largest estuarine system (GBM, Ganges-Brahmaputra-Meghna) in Bangladesh, is a major spawning ground of national fish, Hilsha shad. In this study, we collected 24 surface sediment and 24 water samples from the entire lower estuary (4 sites, 3 sampling points from each site, 2 replicas from each sampling point) to detect trace/heavy metals. Sediment samples were collected from the top surface soil (0-5 cm) using Ekman grab sampler and water samples from 5 cm below the surface layer using plastic water bottles. After collection, sediment and water samples were preserved as necessary using HNO3 (for water). Immediately after reaching the laboratory, sediment samples were dried in an oven at 70°C until the constant weight gained. The metals were then analyzed using energy-dispersive X-ray fluorescence method (EDXRF) and calculated the metal concentrations. In total, 12 metals were detected and the average value (mg/Kg) of all metals for sediment samples followed the descending order of Fe > Ca > K >Ti >Sr >Zr >Rb> Cu > Zn >Pb >As > Ni, and for water the order (µg/mL) of Fe >Ti > Ca > Co >Mn > Ni > Zn >Sr > Cu > As > Se . Besides, several physicochemical parameters i.e. water pH, soil pH, temperature, salinity, dissolved oxygen, hardness, and alkalinity of the 12 sampling points were also measured in-situ using handheld instruments.
In this study, we have analyzed how geo-ecological cues for endangered Olive Ridley turtles' mass nesting behavior got modified by impact of four severe cyclones during 2010-2019 that made landfall in the vicinity of Rushikulya estuary, which is one of the largest mass nesting congregation (arribada) sites in the world. Analyzing last 10 years of shoreline dynamics, we show that even the slightest modification in beach morphology influenced their nesting behavior in Rushikulya rookery. Shoreline change analysis showed periodic phases of high/low erosion and the northward longshore sediment movement, which becomes impeded by the southern spit, the length of which increased by about 1800 m. During the analyzed period, the nesting behavior of Olive Ridley turtle was greatly influenced by changes in land use and land cover pattern around the Rushikulya rookery. Such reductions in tree cover and marshy land areas were majorly driven by anthropogenic activities and extreme weather events, such as cyclones. We also report increased mortality of turtles, no or false mass nesting events due to significant loss and/or erosion of the nesting sites due to cyclones. The results indicate that conservation of Olive Ridley turtles should be more holistic, or ecosystem centric, rather than species centric. It is important to maintain the ecological integrity of their habitat for highly synchronized mass nesting event and eventually their survival.
Mangroves provide essential ecosystem services including coastal protection by acting as coastal greenbelts; however, human-driven anthropogenic activities altered their existence and ecosystem functions worldwide. In this study, the successive degradation of the second largest mangrove forest, Chakaria Sundarbans situated at the northern Bay of Bengal part of Bangladesh was assessed using remote sensing approaches. A total of five multi-temporal Landsat satellite imageries were collected and used to observe the land use land cover (LULC) changes over the time periods for the years 1972, 1990, 2000, 2010, and 2020. Further, the supervised classification technique with the help of support vector machine (SVM) algorithm in ArcGIS 10.8 was used to process images. Our results revealed a drastic change of Chakaria Sundarbans mangrove forest, that the images of 1972 were comprised of mudflat, waterbody, and mangroves, while the images of 1990, 2000, 2010, and 2020 were classified as waterbody, mangrove, saltpan, and shrimp farm. Most importantly, mangrove forest was the largest covering area a total of 64.2% in 1972, but gradually decreased to 12.7%, 6.4%, 1.9%, and 4.6% for the years 1990, 2000, 2010, and 2020, respectively. Interestingly, the rate of mangrove forest area degradation was similar to the net increase of saltpan and shrimp farms. The kappa coefficients of classified images were 0.83, 0.87, 0.80, 0.87, and 0.91 with the overall accuracy of 88.9%, 90%, 85%, 90%, and 93.3% for the years 1972, 1990, 2000, 2010, and 2020, respectively. By analyzing normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), and transformed difference vegetation index (TDVI), our results validated that green vegetated area was decreased alarmingly with time in this study area. This destruction was mainly related to active human-driven anthropogenic activities, particularly creating embankments for fish farms or salt productions, and cutting for collection of wood as well. Together all, our results provide clear evidence of active anthropogenic stress on coastal ecosystem health by altering mangrove forest to saltpan and shrimp farm saying goodbye to the second largest mangrove forest in one of the coastal areas of the Bay of Bengal, Bangladesh.
Macroinvertebrate community in the intertidal setup plays an important role in coastal ecosystem functions and biogeochemical cycle. However, different land use pattern may influence on their community structure, diversity, and composition in the coastal ecosystems. Using Van-Veen grab sampler, 60 sediment samples were seasonally collected from mangroves-dominated, aquaculture-dominated, and anthropogenically affected area in the lower intertidal zone of the Kohelia channel of Bangladesh, the Northern Bay of Bengal. We have tasted the variation in sediment properties across three land-use types in this intertidal habitat. To understand the patterns of benthic macroinvertebrate distribution, a neutral community model was applied. Our results showed that community composition and biodiversity of the benthic macroinvertebrate communities varied significantly between mangrove-dominated area with anthropogenically affected areas among the four seasons. The neutral community model revealed that community assembly of benthic macroinvertebrates in the lower intertidal habitats is structured by stochastic processes while sediment properties have significant influence on species distribution and interactions. Results suggested that land-use changes altered sediment properties and could change the diversity and distribution of the macroinvertebrate communities in the lower intertidal habitats.
The Sustainable Development Goals (SDGs) are a global appeal to protect the environment, combat climate change, eradicate poverty, and ensure access to a high quality of life and prosperity for all. The next decade is crucial for determining the planet's direction in ensuring that populations can adapt to climate change. This study aims to investigate the progress, challenges, opportunities, trends, and prospects of the SDGs through a bibliometric analysis from 2015 to 2022, providing insight into the evolution and maturity of scientific research in the field. The Web of Science core collection citation database was used for the bibliometric analysis, which was conducted using VOSviewer and RStudio. We analyzed 12,176 articles written in English to evaluate the present state of progress, as well as the challenges and opportunities surrounding the SDGs. This study utilized a variety of methods to identify research hotspots, including analysis of keywords, productive researchers, and journals. In addition, we conducted a comprehensive literature review by utilizing the Web of Science database. The results show that 31% of SDG-related research productivity originates from the USA, China, and the UK, with an average citation per article of 15.06. A total of 45,345 authors around the world have contributed to the field of SDGs, and collaboration among authors is also quite high. The core research topics include SDGs, climate change, Agenda 2030, the circular economy, poverty, global health, governance, food security, sub-Saharan Africa, the Millennium Development Goals, universal health coverage, indicators, gender, and inequality. The insights gained from this analysis will be valuable for young researchers, practitioners, policymakers, and public officials as they seek to identify patterns and high-quality articles related to SDGs. By advancing our understanding of the subject, this research has the potential to inform and guide future efforts to promote sustainable development. The findings indicate a concentration of research and development on SDGs in developed countries rather than in developing and underdeveloped countries.
Automated epileptic seizure detection from ectroencephalogram (EEG) signals has attracted significant attention in the recent health informatics field. The serious brain condition known as epilepsy, which is characterized by recurrent seizures, is typically described as a sudden change in behavior caused by a momentary shift in the excessive electrical discharges in a group of brain cells, and EEG signal is primarily used in most cases to identify seizure to revitalize the close loop brain. The development of various deep learning (DL) algorithms for epileptic seizure diagnosis has been driven by the EEG's non-invasiveness and capacity to provide repetitive patterns of seizure-related electrophysiological information. Existing DL models, especially in clinical contexts where irregular and unordered structures of physiological recordings make it difficult to think of them as a matrix; this has been a key disadvantage to producing a consistent and appropriate diagnosis outcome due to EEG's low amplitude and nonstationary nature. Graph neural networks have drawn significant improvement by exploiting implicit information that is present in a brain anatomical system, whereas inter-acting nodes are connected by edges whose weights can be determined by either temporal associations or anatomical connections. Considering all these aspects, a novel hybrid framework is proposed for epileptic seizure detection by combined with a sequential graph convolutional network (SGCN) and deep recurrent neural network (DeepRNN). Here, DepRNN is developed by fusing a gated recurrent unit (GRU) with a traditional RNN; its key benefit is that it solves the vanishing gradient problem and achieve this hybrid framework greater sophistication. The line length feature, auto-covariance, auto-correlation, and periodogram are applied as a feature from the raw EEG signal and then grouped the resulting matrix into time-frequency domain as inputs for the SGCN to use for seizure classification. This model extracts both spatial and temporal information, resulting in improved accuracy, precision, and recall for seizure detection. Extensive experiments conducted on the CHB-MIT and TUH datasets showed that the SGCN-DeepRNN model outperforms other deep learning models for seizure detection, achieving an accuracy of 99.007%, with high sensitivity and specificity.