Environmental Monitoring and Assessment最新文献

Fluoride contamination in groundwater is a serious public health concern, especially in semi-arid regions like Bathinda in Punjab, where people rely heavily on groundwater for drinking and daily use. Despite several studies on fluoride contamination, research integrating uniform spatial sampling, hydrogeochemical assessment, and advanced predictive modeling remains limited. This study addresses that gap by automating groundwater fluoride prediction using deep learning techniques and evaluating seasonal hydrochemical variations in the Bathinda district. The study collected 226 groundwater samples across the pre-monsoon and monsoon seasons using GIS-based sampling at approximately 5-km intervals. Hydrochemical parameters were analyzed following APHA standards, and the Water Quality Index (WQI) was calculated. Fluoride concentrations were spatially mapped using GIS and modeled using both machine learning and deep learning approaches, specifically the Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Deep Neural Network (DNN), and hybrid CNN-LSTM models. To enhance model robustness, data augmentation was applied using the nearest-neighbor interpolation, creating 30,000 synthetic points. Among all models, the DNN outperformed the others, with an R² of 0.92 (pre-monsoon) and 0.91 (monsoon), followed by the hybrid CNN-LSTM. Spatial analysis revealed fluoride hotspots exceeding WHO limits (> 1.5 ppm), strongly associated with specific lithological units, land use land cover (LULC), and geomorphological features. This integrated approach enables accurate fluoride prediction in unsampled areas, supporting early risk identification and informed decision-making. These findings are highly relevant to strategies for groundwater management, environmental monitoring, and public health planning in regions affected by fluoride.

Monitoring the condition and degradation of rangeland ecosystems is essential for sustainable rangeland management. Aboveground biomass density (AGBD) is a key indicator of rangeland health, providing insights into forage availability for pastoralists and climate change mitigation strategies. This work applied open access earth observation data to generate spatially continuous, high-resolution AGBD maps for an important pastoralist area in Ethiopia, where season-specific AGBD assessments remain limited. This study employed recursive feature elimination with cross-validation using random forest (RFECV_RF) with fivefold cross-validated grid search for variable selection and hyperparameter tuning. Convolutional neural network (CNN) and RF regression models were applied to estimate AGBD at 50-m resolution across seasonal (short and main rainy seasons) and combined annual datasets, using spaceborne Global Ecosystem Dynamics Investigation LiDAR measurements (GEDI L4A) data for training and validation. Predictor variables included Sentinel-1/2 backscatter and spectral bands, vegetation indices, topographic factors, and precipitation data. Both models performed well, with CNN consistently outperforming RF. For the combined annual analysis, CNN achieved a root mean square error (RMSE) of 4.77 t/ha, relative RMSE of 46.7%, a mean absolute error (MAE) of 2.55 t/ha, with a slight underestimation bias (mean error, ME -0.22 t/ha), and the coefficient of determination (R²) of 0.93. Errors were lower in the short rainy season (RMSE 4.88, MAE 1.90 t/ha) than in the main rainy season (RMSE 6.99, MAE 3.69 t/ha). This work establishes a novel, scalable framework and provides a critical high-resolution AGBD baseline to detect degradation hotspots and support season-specific strategies for sustainable grazing, restoration, and pastoral resilience.

Sediment load variations are a key component of eco-hydrological processes and are jointly driven by climate change and intensified human activities. Given that hydrological-sedimentary dynamics in the Yellow River Basin profoundly affect China's ecological security, quantitatively distinguishing climatic and anthropogenic contributions to sediment load changes is of particular importance. To address this, climatic and anthropogenic contributions to sediment load variations across river reaches were quantified by integrating the double mass curve (DMC) method with elastic coefficient analysis based on the fractal-Budyko framework, using hydrological, meteorological, and anthropogenic datasets from the Yellow River mainstem spanning 1961-2022. In this study, climate change is mainly reflected through variations in precipitation and potential evapotranspiration, whereas human activities are defined as anthropogenic interventions affecting sediment transport through land-surface change and water-sediment regulation. The main findings are as follows. Except for the reach above Tangnaihai, sediment loads along the mainstem have decreased significantly over the past six decades, with reductions during summer and autumn dominating interannual variations. In terms of water-sediment relationships, runoff exerted a stronger influence on sediment load than precipitation; however, its effect weakened over time, indicating intensifying anthropogenic interference. Attribution analysis shows that during 1981-2000, climate change dominated sediment variations in the Tangnaihai headwater region, with contribution rates of 88.15-98.45%, whereas human activities were the primary drivers in the mid- and downstream reaches, contributing 84.67-93.62%. During 2001-2022, the contribution of human activities further increased across the basin, particularly in the Tangnaihai headwater region, where it reached 66.41-72.67%. Overall, the Yellow River Basin exhibits pronounced spatiotemporal heterogeneity in sediment dynamics, with a progressive shift from climate-dominated to human-dominated controls.

Providing robust real-time flood warnings is of paramount importance to coastal communities. Although state-of-the-art hydrodynamic models are capable of robustly predicting coastal water levels (CWL), unresolved drivers affecting water level fluctuations are often not represented by the model governing equations. This work evaluates a novel method to improve the performance of the ADvanced CIRCulation (ADCIRC) hydrodynamic model by assimilating observations from four nadir-only satellite altimetry missions with a set of National Oceanic and Atmospheric Administration (NOAA) gauge stations located across the entire U.S. East Coast. Two different types of simulations were performed - open loop (OL) and data assimilation (DA). Five different simulations were performed in which four different satellite altimetry observations were assimilated individually and under two different scenarios - with and without considering the data quality flags. Results indicate that, despite their limited spatial coverage, merging nadir-only observations into ADCIRC from thenadir altimeter of the Surface Water and Ocean Topography (SWOT) can improve the model performance at 76% of the gauge locations, whereas Sentinel-6 improves it at 73% of the total locations, Jason-3 at 74%, and SARAL at 21%. Furthermore, combining observations from SWOT-nadir, Jason-3, and Sentinel-6 can improve the ADCIRC performance at more than 80% of the gauge locations for a 107-day simulation. Nadir-only satellite altimetry observations can be useful for improving the model performance even if flagged as "poor quality" near the coast. When the flagged data are disregarded, SWOT can improve ADCIRC at 78% of the gauge locations, Sentinel-6 at 73%, Jason-3 at 53%, and SARAL at 21%. The ability to improve the model simulations largely depends on the availability of a nearby satellite overpass. Therefore, model performance can be further enhanced if satellite observations are available during a storm surge event, stressing the importance of frequent satellite overpasses. RESEARCH HIGHLIGHTS: Nadir-only satellite altimetry improves storm surge model performance. Model skill increases when overpasses capture surge events. Multi-mission altimetry assimilation yields the highest overall accuracy.

Freshwater lakes are critical sources of drinking water worldwide, yet contamination by trace elements (TEs) presents significant health risks. Phewa Lake, Nepal, a Ramsar-listed wetland supporting many people, irrigation, fisheries, and tourism, was selected as a case study due to its socio-economic and ecological value. This study investigates the spatiotemporal distribution and health risks of 10 TEs across pre-monsoon and monsoon seasons. 50 lake water samples were collected, split evenly between pre-monsoon and monsoon. Results showed elevated concentrations of arsenic (As), chromium (Cr), copper (Cu), manganese (Mn), nickel (Ni), lead (Pb), and zinc (Zn) during the pre-monsoon, with values declining by approximately half during the monsoon due to rainfall dilution. Spatially, higher concentrations were observed near urban settlements and drainage points. Subsequent statistical analyses identified geogenic sources as predominant, with minor anthropogenic influence mapped to urban shorelines. Water quality was assessed using the Water Quality Index (WQI) and Metal Index (MI): scores were 9.66 and 0.09 pre-monsoon, and 2.86 and 0.03 during monsoon, all well within the World Health Organization (WHO) guideline limits. Hazard Index (HI) values for all TEs were below unity, with As posing the highest non-carcinogenic risk (HI_children = 0.115, HI_adults = 0.076). Cancer risk was low to medium for Pb, Cr, and As. Although water quality was generally acceptable with low risks, proactive measures, such as routine monitoring, regulated runoff, and improved wastewater treatment in alignment with Sustainable Development Goals (SDGs) 6, 13, and 15, are recommended. These findings can inform sustainable urban lake management in the Himalayas and comparable regions globally.