Search Results (298)

Heavy metal pollution from human activities is an increasing environmental concern. This study investigates the concentrations of Cu, Pb, Zn, Cd, Hg, and As in the coastal seawater offshore of Beihai, Guangxi, in April 2021, and explores their relationships with dissolved inorganic nitrogen, phosphate, and salinity. Our results reveal higher heavy metal concentrations in the northern nearshore waters and lower levels in southern offshore areas, with surface waters generally exhibiting greater enrichment than bottom waters. Surface concentrations show a decreasing trend from the northeast to the southwest, likely influenced by prevailing northeast monsoon winds. While bottom water concentrations decline from the northwest to the southeast, which indicates the influence of riverine runoff, particularly from the Qinzhou Bay estuary. Heavy metal levels in southern Beihai waters are comparable to those in the Beibu Gulf, except for Hg and Zn, which are significantly higher in the water of the Beibu Gulf. Notably, heavy metal concentrations in both Beihai and Beibu Gulf remain considerably lower than those observed in the coastal waters of Guangdong. Overall, Beihai’s coastal seawater meets China’s Class I quality standards. Nonetheless, continued monitoring is essential, especially of the potential ecological impacts of Hg and Zn on marine life. Full article

In recent years, remote sensing and geographic information systems (GISs) have become essential tools for effective landscape management. This study utilizes these technologies to analyze land use and land cover (LULC) changes in Dibru-Saikhowa National Park, a riverine ecosystem in Assam, India, from its designation as a national park in 2000 through 2024. The satellite imagery was used to classify LULC types and track landscape changes over time. In 2000, grasslands were the dominant land cover (28.78%), followed by semi-evergreen forests (25.58%). By 2013, shrubland became the most prominent class (81.31 km²), and degraded forest expanded to 75.56 km². During this period, substantial areas of grassland (29.94 km²), degraded forest (10.87 km²), semi-evergreen forest (12.33 km²), and bareland (10.50 km²) were converted to shrubland. In 2024, degraded forest further increased, covering 80.52 km² (23.47%). This change resulted since numerous areas of shrubland (11.46 km²) and semi-evergreen forest (27.48 km²) were converted into degraded forest. Furthermore, significant shifts were observed in grassland, shrubland, and degraded forest, indicating a substantial and consistent decline in grassland. These changes are largely attributed to recurring Brahmaputra River floods and increasing anthropogenic pressures. This study recommends a targeted Grassland Recovery Project, control of invasive species, improved surveillance, increased staffing, and the relocation of forest villages to reduce human impact and support community-based conservation efforts. Hence, protecting the landscape through informed LULC-based management can help maintain critical habitat patches, mitigate anthropogenic degradation, and enhance the survival prospects of native floral and faunal assemblages in DSNP. Full article

Heavy metal contamination in groundwater has emerged as a significant environmental issue, driven by rapid industrialization and intensified human activities, particularly in southern China. Heavy metal pollution in groundwater often presents complex spatial patterns and multiple sources; understanding the spatial heterogeneity and controlling factors of heavy metals is crucial for pollution prevention and water resource management in industrial regions. This study applied spatial autocorrelation analysis and self-organizing maps (SOM) coupled with K-means clustering to investigate the spatial distribution and key influencing factors of nine heavy metals (Cr, Fe, Mn, Ni, Cu, Zn, As, Ba, and Pb) in a typical industrial area in southern China. Heavy metals show significant spatial heterogeneity in concentrations. Cr, Mn, Fe, and Cu form local hotspots near urban and peripheral zones; Ni and As present downstream enrichment along the river pathway with longitudinal increase trends; Zn, Ba, and Pb exhibit a fluctuating pattern from west to east in the piedmont region. Local Moran’s I analysis further revealed spatial clustering in the northwest, riverine zones, and coastal outlet areas, providing insight into potential source regions. SOM clustering identified three types of groundwater: Cluster 1 (characterized by Cr, Mn, Fe, and Ni) is primarily influenced by industrial pollution and present spatially scattered distribution; Cluster 2 (dominated by As, NO₃⁻, Ca²⁺, and K⁺) is associated with domestic sewage and distributes following river flow; Cluster 3 (enriched in Zn, Ba, Pb, and NO₃⁻) is shaped by agricultural activities and natural mineral dissolution, with a lateral distribution along the piedmont zone. The findings of this study provide a scientific foundation for groundwater pollution prevention and environmental management in industrialized areas. Full article

The coastline of the Yellow River Delta in China has experienced significant dynamic changes due to both natural and human activities. Investigating its coastal dynamics and understanding the equilibrium with riverine runoff and sediment discharge is crucial for ecological balance and sustainable development in the region. In this study, a coastline extraction algorithm was developed by integrating water index and dynamic frequency thresholds based on the Google Earth Engine platform. Long-term optical remote sensing datasets from Landsat (1988–2016) and Sentinel-2 (2017–2023) were utilized. The End Point Rate (EPR) and Linear Regression Rate (LRR) methods were employed to quantify coastline changes, and the relationship between coastal evolution and runoff–sediment dynamics was investigated. The results revealed the following: (1) The coastline of the Yellow River Delta exhibits pronounced spatiotemporal variability. From 1988 to 2023, the Diaokou estuary recorded the lowest EPR and LRR values (−206.05 m/a and −248.33 m/a, respectively), whereas the Beicha estuary recorded the highest values (317.54 m/a and 374.14 m/a, respectively). (2) The cumulative land area change displayed a fluctuating pattern, characterized by a general trend of increase–decrease–increase, indicating a gradual progression toward dynamic equilibrium. The Diaokou estuary has been predominantly erosional, while the Qingshuigou estuary experienced deposition prior to 1996, followed by subsequent erosion. In contrast, the land area of the Beicha estuary has continued to increase since 1997. (3) Deltaic progradation has been primarily governed by runoff–sediment dynamics. Coastline advancement has occurred along active river channels as a result of sediment deposition, whereas former river mouths have retreated landward due to insufficient fluvial sediment input. In the Beicha estuary, increased land area has exhibited a strong positive correlation with annual sedimentary influx. The critical sediment discharge required to maintain equilibrium has been estimated at 79 million t/a for the Beicha estuary and 107 million t/a for the entire deltaic region. These findings provide a scientific foundation for sustainable sediment management, coastal restoration, and integrated land–water planning. This study supports sustainable coastal management, informs policymaking, and enhances ecosystem resilience. Full article

Previous studies have consistently reported the detrimental impact of dam construction on natural populations of softshell turtles across East and Southeast Asia, with particularly severe effects on large-bodied species. The Wattle-necked Softshell Turtle (Palea steindachneri), a large-sized and Critically Endangered member of the family Trionychidae, remains poorly documented throughout much of its native range in Southeast Asia. In this study, we present new field data from the Đà River basin in northern Vietnam, encompassing areas both upstream and downstream of the Sơn La Dam. Data were obtained through a combination of direct field observations, camera trap monitoring, and semi-structured interviews with local fishers and traders. Two individuals of P. steindachneri—including a juvenile—were recorded, providing the first confirmed evidence of ongoing natural reproduction in the region. Additionally, we documented 102 individuals of Pelodiscus sp., encompassing all life stages and indicating a stable, reproducing local population. Despite overlapping in macrohabitat use along the river, the two species were spatially segregated, with a minimum interspecific distance of 8.2 km, suggesting broad sympatry without syntopy, potentially due to microhabitat partitioning. These findings underscore the persistence and likely reproductive viability of P. steindachneri in modified riverine systems affected by dams, and have broader conservation implications for other threatened taxa with similar ecologies, such as Rafetus swinhoei. Urgent conservation actions, including habitat protection, community-based monitoring, and strengthened regulation of the wildlife trade, are essential to ensure the survival of remaining wild populations. Full article

Anthropogenic debris in urban floodplains poses significant environmental and ecological risks, with an estimated 4 to 12 million metric tons entering oceans annually via riverine transport. While remote sensing and artificial intelligence (AI) offer promising tools for automated debris detection, most existing datasets focus on marine environments with homogeneous backgrounds, leaving a critical gap for complex terrestrial floodplains. This study introduces the San Diego River Debris Dataset, a multi-resolution UAV imagery collection with ground reference designed to support automated detection of anthropogenic debris in urban floodplains. The dataset includes manually annotated debris objects captured under diverse environmental conditions using two UAV platforms (DJI Matrice 300 and DJI Mini 2) across spatial resolutions ranging from 0.4 to 4.4 cm. We benchmarked five deep learning architectures (RetinaNet, SSD, Faster R-CNN, DetReg, Cascade R-CNN) to assess detection accuracy across varying image resolutions and environmental settings. Cascade R-CNN achieved the highest accuracy (0.93) at 0.4 cm resolution, with accuracy declining rapidly at resolutions above 1 cm and 3.3 cm. Spatial analysis revealed that 51% of debris was concentrated within unsheltered encampments, which occupied only 2.6% of the study area. Validation confirmed a strong correlation between predicted debris extent and field measurements, supporting the dataset’s operational reliability. This openly available dataset fills a gap in environmental monitoring resources and provides guides for future research and deployment of UAV-based debris detection systems in urban floodplain areas. 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

The flood risk of urban buildings has been continuously increasing, owing to the increasing frequency and severity of floods. There is an urgent need to implement precise mitigation strategies to address the unique characteristics of urban residential structures. In this study, an indicator system consisting of 17 indicators in four dimensions (extent of hazard, degree of exposure, vulnerability, and response ability) was developed for the flood risk of residential buildings. The assessment was conducted in Katsushika Ward, Tokyo, and the ANALYTIC HIERARCHY PROCESS(AHP)—Criteria Importance Through Intercriteria Correlation (CRITIC) method was integrated with Geographic Information System(GIS) technology. The spatial distribution of residential flood risk exhibits marked heterogeneity, with ‘extremely high’ and ‘high’ risk areas concentrated in northwestern and southwestern riverine zones. These regions exhibit dense populations, substantial assets, deep immersion depths, prolonged inundation durations, high proportions of wooden houses, and narrow roads impeding rescue operations. The mitigation priorities are the following: Enhance flood-resistant building heights and quality in riverside areas, strengthen vacant house management, widen rescue access routes, promote mid-/high-rise buildings, and optimize subsidies for tenants and single-person households to minimize losses. Full article

This investigation examined the physicochemical characteristics and heavy metal contamination within the surface sediments and aquatic environments of Pattani Bay, Thailand, throughout both wet and dry seasons. The sediments were primarily composed of fine-grained materials, specifically silt and clay, and exhibited greater propensity to absorb heavy metals from water. Notably elevated concentrations of Cd and Pb were detected, particularly within riverine sediment deposits. This indicates that riverine inputs are significant pathways of the contamination and potentially associated with historical mining activities. Seasonal fluctuations affected physicochemical parameters as well as metal concentrations. The heightened levels of Cd and Pb during the wet season were attributed to runoff phenomena. Pollution indices including the Contamination Factor (CF), pollution load index (PLI), and geoaccumulation index (Igeo) demonstrated moderate to extremely high contamination levels of Cd and Pb in certain areas. The Principal Component Analysis (PCA) suggested possible similar sources for multiple metals including Cd, Cu, Pb, and Zn. The results showed that the heavy metal pollution present is serious, especially for Cd and Pb. These could lead to high ecological health risks and so it is necessary to focus on implementing environmental management strategies for Pattani Bay. Full article

The Ganga River, a lifeline for millions and a critical freshwater ecosystem, is under threat from escalating plastic pollution driven by widespread usage and inadequate disposal practices. While marine ecosystems have garnered extensive research attention, freshwater systems—particularly in the Global South—remain underexplored, leaving critical gaps in understanding plastic pollution’s sources and pathways. Addressing these gaps, the study documents the prevalence and typology of plastic debris in urban and underexplored rural communities along the Ganga River, India, aiming to suggest mechanisms for a reduction in source-based pollution. A stratified random sampling approach was used to select survey sites and plastic debris was quantified and categorised through transect surveys. A total of 37,730 debris items were retrieved, dominated by packaging debris (52.46%), fragments (23.38%), tobacco-related debris (5.03%), and disposables (single-use plastic cutleries) (4.73%) along the surveyed segments with varying abundance trends. Floodplains displayed litter densities nearly 28 times higher than river shorelines (6.95 items/m² vs. 0.25 items/m²), with minor variations between high- and low-population-density areas (7.14 items/m vs. 6.7 items/m²). No significant difference was found between rural and urban areas (V = 41, p = 0.19), with mean densities of 0.87 items/m² and 0.81 items/m², respectively. Seasonal variations were insignificant (V = 13, p = 0.30), but treatment sites displayed significant variance (Chi² = 10.667, p = 0.004) due to flood impacts. The findings underscore the urgent need for tailored waste management strategies integrating industrial reforms, decentralised governance, and community-driven efforts. Enhanced baseline information and coordinated multi-sectoral efforts, including Extended Producer Responsibility (EPR), are crucial for mitigating plastic pollution and protecting freshwater ecosystems, given rivers’ significant contribution to ocean pollution. Full article

Hydrological extremes, particularly floods, are becoming prevalent in parts of Nigeria. During the 2022 rainy season, Nigeria experienced a devastating riverine flood with severe societal impacts. However, the principal factors contributing to riverine flooding in Nigeria remain debatable, necessitating data-driven and policy-relevant studies to quantify the primary causes of riverine floods in Nigeria. In this study, we applied remote sensing techniques and climate data to characterize the 2022 flood event in Nigeria by quantifying the flooded areas, the number of people affected per state, and riverine flood risk assessment. We investigated rainfall and soil moisture anomalies during the flood event and inferred the contribution of the opening of the Lagdo Dam, in Cameroon, to the severity of the flood event. Our results show that large parts of Cameroon and northern Nigeria experienced above-average rainfall during the 2022 rainy season, contributing to soil saturation. About 50,000 ha of land were flooded in Nigeria between July and August; however, following the opening of the Lagdo Dam in September, the flood extent spiked to 200,000 ha (i.e., about 300% increase), suggesting that excess water from the Lagdo Dam, coupled with inadequate drainage infrastructure, amplified the flood extent in Nigeria. Flooded areas were more extensive in northern Nigeria than in southern regions; however, due to denser settlements in flood-prone areas, Anambra State in southeastern Nigeria was the most affected in terms of people impacted. Therefore, besides rainfall changes and inadequate drainage infrastructures leading to the inundation of the major rivers in Nigeria and their tributaries, we also ranked poor town planning against the population density per square meter as a critical factor that amplifies the societal impacts of flooding in Nigeria. Finally, based on the 2022 conditions and the available pre-flood population data, an estimated number of 105,000 people are at critical risk of riverine flooding in Nigeria. Full article

Deep learning models offer innovative solutions for cervical cancer screening in vulnerable regions such as the Brazilian Amazon. These tools are particularly relevant in areas with limited access to healthcare services, where the high prevalence of the disease severely affects riverine and indigenous populations. Artificial intelligence can overcome the limitations of traditional screening methods, providing faster and more accurate diagnoses. This enables early disease detection and reduces mortality, improving equitable access to healthcare. Furthermore, the application of these technologies complements global efforts to eliminate cervical cancer, aligning with the WHO strategies. This review emphasizes the need for model adaptation to local realities, which is essential to ensure their effectiveness in low-infrastructure areas, reinforcing their potential to reduce health disparities and expand access to quality diagnostics. Full article

This study investigated the impact of land use change on water quality in the Xinxian River Basin amidst rapid urbanization. While previous studies have predominantly focused on single-scale buffer analyses or specific land use types, the interactions between multi-scale riparian buffers and diverse land cover dynamics remain rarely understudied, particularly in a rapidly urbanizing county in the Yangtze River Basin. Land use type data for the Xinxian River Basin in 2000, 2010, and 2020 were acquired using GIS technology, and subsequent analysis quantified land use pattern changes over this 20-year period. Additionally, 2023 land use data for riparian buffer zones (50 m, 100 m, 200 m, 400 m, and 600 m) were obtained via GIS and subjected to Redundancy Analysis (RDA) with 2023 water quality monitoring data to evaluate the impact of land use on water quality. The results revealed significant land use conversion dynamics, particularly between natural and anthropogenic cover types. Forest cover exhibited negative correlations with riverine nutrient concentrations, while built-up areas displayed strong positive associations, especially at finer scales (50–100 m buffers). Notably, the dominant influencing factor shifted from built-up land at smaller buffer scales (50–100 m) to forest land at larger scales (400–600 m), whereas agricultural land showed no significant correlation. These findings highlight scale-dependent relationships between land use and aquatic ecosystems, emphasizing the critical role of spatial planning in mitigating urbanization impacts. The work is conducive to the sustainable development of Longgan Lake National Wetland Nature Reserve and the protection of water ecology in the middle and lower reaches of the Yangtze River. Full article

This study investigates the potential increase in flood hazard in the city of Elbląg, Poland, due to the climate-induced intensification of wind surges in the Vistula Lagoon. Using the HEC-RAS 2D (version 6.6) model—typically applied to riverine systems but here adapted for wind-driven lagoon dynamics—we simulate both historical and hypothetical storm events to evaluate water level changes under varying wind speeds. Model validation was performed using the January 2019 surge event, demonstrating strong agreement with observed water levels (NSE > 0.93). Subsequent simulations using synthetic wind scenarios show that extreme NE winds of 35 m·s⁻¹ could raise water levels above 3.5 m asl, significantly surpassing warning and alarm thresholds. The results reveal a non-linear response between wind speed and water accumulation, underscoring the elevated hazard for low-lying areas such as Żuławy Elbląskie. The novelty of this study lies in the innovative application of HEC-RAS to a wind-driven lagoon environment and in the generation of synthetic surge scenarios for climate resilience planning. These findings provide critical insight for improving flood risk assessment and infrastructure adaptation in the face of ongoing climate change. Full article

Water hardening and NO₃⁻ pollution have affected water quality globally. These environmental problems threaten social sustainability and human health, especially in piedmont agricultural areas. The aim of this study is to determine the biogeochemical mechanisms of HCO³–Ca water and NO₃⁻ pollution in a typical piedmont agricultural area (Qingshui River, Zhangjiakou, China). Here, an extensive biogeochemical investigation was conducted in a typical piedmont agricultural area (Qingshui River, China) using multiple hydrochemical, isotopic (δ²H-H₂O, δ¹⁸O-H₂O and δ¹³C-DIC) and molecular-biological proxies in combination with a forward model. In the region upstream of the Qingshui River, riverine hydrochemistry was dominated by HCO₃–Ca water, with only NO₃⁻ concentrations (3.08–52.8 mg/L) exceeding the acceptable limit (10 mg/L as N) for drinking water quality. The riverine hydrochemistry responsible for the formation of HCO₃–Ca water was mainly driven by carbonate dissolution, with a contribution rate of 49.8 ± 3.96%. Riverine NO₃⁻ was mainly derived from agricultural NH₄⁺ emissions rather than NO₃⁻ emissions, originating from sources such as manure, domestic sewage, soil nitrogen and NH₄⁺-synthetic fertilizer. Under the rapid hydrodynamic conditions and aerobic water environment of the piedmont area, NH₄⁺-containing pollutants were converted to HNO₃ by nitrifying bacteria (e.g., Flavobacterium and Fluviimonas). Carbonate (especially calcite) was preferentially and rapidly dissolved by the produced HNO₃, which was attributed to the strong acidity of HNO₃. Therefore, higher levels of Ca²⁺, Mg²⁺, HCO₃⁻ and NO₃⁻ were simultaneously released into river water, causing riverine HCO₃–Ca water and NO₃⁻ pollution in the A-RW. In contrast, these biogeochemical mechanisms did not occur significantly in the downstream region of the river due to the cement-hardened river channels and strict discharge management. These findings highlight the influence of agricultural HNO₃ on HCO₃–Ca water and NO₃⁻ pollution in the Qingshui River and further improve the understanding of riverine hydrochemical evolution and water pollution in piedmont agricultural areas. Full article