Search Results (4,759)

In sub-Saharan Africa, intensive pesticide use in irrigated agriculture is threatening the quality of soil, water bodies and ecosystem services, yet integrated risk assessments remain limited. This study evaluated the environmental implications and risks of pesticide residues in soils (0–20 cm; n = 15) and irrigation water (n = 15) from off-season irrigation area of the Goulbi Maradi Valley, Niger. Twelve commonly used pesticides in Djiratawa, Maradi 3 and Tibiri, were quantified by High-Performance Liquid Chromatography with Variable Wavelength Detector (HPLC-VWD), revealing Tibiri as a contamination hotspot, where the total pesticide residues in soil and irrigation water reached 6.4 and 19.7 times the respective European Union soil and drinking water benchmarks, dominated by Cypermethrin, Emamectin benzoate and Chlorpyrifos ethyl in soils, and Emamectin benzoate and Dichlorvos in water. Multivariate analysis showed that soil particle size, particularly higher clay content, controlled the retention of strongly sorbing compounds, while pH and salinity governed the occurrence of more soluble residues in irrigation water. While non-carcinogenic risks for Adults and Children via soil and water exposure were acceptable (Hazard Quotient and Hazard Index < 1), ecological risks were unacceptable, with Folsomia candida and Daphnia magna the most affected organisms, driven by Emamectin benzoate (Toxicity Exposure Ratio < 2). Priority actions include phasing out Dichlorvos and Paraquat dichloride, tightening controls on Emamectin benzoate and expanding food-chain monitoring, particularly in vegetables and fish, to support multi-trophic risk assessment and safer irrigation management. Full article

This study assessed the impact of pandemic-related changes in treated wastewater on surface water quality and ecological status of the Raba River within the Natura 2000 site. Particular attention to the reliability of the Kasinka Mała wastewater treatment plant operating in this protected area during the two study periods—pre-pandemic (PP) and COVID-19 (CP)—was given. For this purpose, current standard monitoring methods (ecological status of a small flysch stream, existing and potential threats to the Natura 2000 site) and extended monitoring methods (river’s utility values, technological reliability of the treatment plant operating with SBR technology, reliability rating of the river as a sewage receiver) were used. The results indicated that biodegradable carbon compounds (as dissolved and suspended forms) and ammonium nitrogen were the dominant factors determining water quality. Their presence reduced the Raba River’s utility value—determined by what is required of surface water treatment—by at least one class. During the CP, the reliability analysis showed that the river remained in a reduced class for 145 days due to elevated BOD₅ and nearly one-third of the year due to elevated TSS levels. For approximately half of the year, ammonium nitrogen concentrations exceeded the threshold of 1.8 mg·dm⁻³, thereby further reducing the class of water quality. Technological reliability of the WWTP during PP for BOD₅, COD, TSS, NH₄⁺–N, and PO₄⁻³–P was 43%, 100%, 30%, 86%, and 100%, respectively. This means that permitted values of COD and PO₄⁻³–P were maintained. The exceedances of limits concerned BOD₅ (25 mg O₂·dm⁻³ for 208 days), TSS (35 mg O₂·dm⁻³ for 256 days), and NH₄⁺–N (15 mg O₂·dm⁻³ for 51 days). During CP, the technological reliability of the WWTP decreased rapidly for the following pollutants to 5%, 18%, 18%, 30%, and 89%, respectively. This means that permissible concentrations of BOD₅ (25 mg O₂·dm⁻³ for 347 days), COD (125 mg O₂·dm⁻³ for 241 days), TSS (35 mg O₂·dm⁻³ for 299 days), NH₄⁺–N (15 mg O₂·dm⁻³ for 256 days), and PO₄⁻³–P (2 mg O₂·dm⁻³ for 40 days) were exceeded. A two-year monitoring campaign has shown that small flysch rivers receiving treated wastewater may experience prolonged changes in water quality under conditions of increased anthropopressure. Effective ecosystem protection should, therefore, include extended monitoring and stricter management of BOD₅, TSS, and NH₄⁺–N in SBR systems in protected areas. Full article

Riverine systems in tropical deltaic environments are increasingly exposed to hydrological variability driven by climate change, sea level rise, and extreme precipitation. In Nigeria’s Niger Delta, recurrent flooding and environmental degradation are intensifying pressures on freshwater ecosystems and dependent communities. This study examines hydrological stressors in riverine settlements of Bayelsa State and explores associated socio-ecological responses. Using an exploratory qualitative design, data were collected from 51 women residing in highly vulnerable riverine communities through 24 in-depth interviews and three focus group discussions. Thematic analysis identified prolonged flooding, riverbank erosion, salinity intrusion, water quality deterioration, and oil pollution, as key drivers of declining fisheries, reduced agricultural productivity, and household water insecurity. These stressors have prompted relocation, livelihood diversification, and reliance on indigenous adaptation practices. The study recommends: (1) installation of community-based flood early warning systems; (2) routine monitoring of surface water quality and salinity; (3) enforcement of oil spill remediation and pollution control measures; (4) rehabilitation of wetlands and natural drainage channels; and (5) targeted support for climate-resilient livelihoods such as aquaculture and elevated farming systems. These measures are critical for sustaining freshwater ecosystems and strengthening resilience in vulnerable deltaic communities. Full article

The use of microbial consortia has emerged as a promising strategy to improve crop performance under abiotic stress, although their effectiveness under field conditions remains variable. Here, we evaluated whether plant growth-promoting (PGP) bacterial consortia assembled based on synergistic PGP traits can improve physiological performance, yield, and grain quality of yellow lupine (Lupinus luteus L.) under field-based drought conditions. A semi-controlled, field-based pot experiment was conducted under contrasting water regimes (irrigated and drought) to evaluate four rhizobacterial strains (Microbacterium sp. S13.2, Variovorax sp. S14.7, Bacillus sp. S31, and Lysinibacillus sp. S34), assembled into four consortia: two characterized by high (C1 and C2) and two by low (C3 and C4) auxin production and ACC deaminase activity, along with an uninoculated control. Physiological responses were monitored across phenological stages through stomatal conductance and photosynthetic pigments, while agronomic traits, yield components, and grain quality were assessed at harvest. Inoculation effects were stage-dependent and became more evident under drought conditions. Consortia C1(Microbacterium sp. S13.2 + Variovorax sp. S14.7) and C2 (Bacillus sp. S31 + Lysinibacillus sp. S34) consistently improved biomass accumulation, seed number, and grain yield compared to the uninoculated control, whereas C3 (Lysinibacillus sp. S34 + Variovorax sp. S14.7) and C4 (Bacillus sp. S31 + Variovorax sp. S14.7) showed limited or neutral effects. Multivariate analysis indicated distinct performance strategies, with C1 associated with higher productivity and C2 with improved yield stability under drought. Grain quality parameters remained stable across treatments. These results show that cooperative microbial consortia can improve lupine performance under water-limited conditions, and their effectiveness depends on the functional interactions among consortium members. Full article

Climate warming in Mediterranean vineyards accelerates grape ripening and increases the incidence of sunburn and berry shriveling, leading to imbalances in grape composition and wine quality. This study evaluated the combined effects of a non-positioned training system (asymmetric sprawl) and foliar application of kaolin particle film on vine microclimate, agronomic performance and wine aroma profile in a Syrah cv. vineyard under warm conditions. Vine canopy temperature was monitored by UAV thermography at veraison and harvest, while grape damage, yield components and vegetative balance were assessed at harvest. Wines obtained from each treatment were analysed for chemical composition, volatile compounds and sensory attributes. Kaolin application significantly reduced canopy temperature, particularly under water-limited conditions at veraison (up to 1.9 °C), and the combination with sprawl training decreased the proportion of sunburnt and shrivelled clusters. These microclimatic modifications were associated with higher ethanol content, improved colour intensity and increased total polyphenol index in wines. The combined strategy also enhanced the concentration of key aroma compounds, especially terpenes and fruity esters, resulting in higher values of citrus, floral and fruity aromatic series. Sensory evaluation confirmed a better overall appreciation of wines produced from vines managed with both practices. Overall, the integration of canopy architecture modification and reflective particle film represents an effective strategy to mitigate heat stress effects in warm viticultural regions, improving grape physiological performance and contributing to the preservation of wine aromatic quality under climate change scenarios. Full article

Heavy metal contamination in agricultural production is a significant public health issue in Mexico, as it directly impacts food safety and population exposure through dietary intake. Available scientific evidence indicates that vegetables and other plant-derived foods can serve as significant exposure pathways for toxic elements such as arsenic, cadmium, lead, chromium, and mercury. The consumption of contaminated foods may contribute to cumulative adverse health effects, including neurological, renal, and reproductive alterations, as well as an increased risk of chronic diseases. In Mexico, risk assessment is further constrained by methodological heterogeneity across studies and by difficulties in translating scientific evidence into concrete regulatory actions. Critically, the national regulatory framework lacks specific standards establishing maximum permissible limits for heavy metals in fresh fruits, vegetables, and grains, despite their central role in the population’s diet. Regulations focus primarily on drinking water quality and selected processed foods, creating a regulatory gap in the direct control of contaminants in crops. The findings underscore the urgent need to strengthen public policies by establishing crop-specific regulatory standards, implementing systematic monitoring programs, and integrating food safety considerations more effectively into environmental, agricultural, and public health policies in Mexico. Full article

The preparation for next-generation Earth Observation missions, such as the European Space Agency’s (ESA) Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) and Land Surface Temperature Monitoring (LSTM), requires robust pre-launch proxy datasets. Because current simulation methodologies frequently rely on isolated, platform-specific approaches, this study proposes a comprehensive, unified multisensor framework capable of dynamically generating operationally realistic CHIME and LSTM datasets from diverse airborne and satellite sources. Three distinct processing pipelines were established. For hyperspectral data simulation, precursor satellite imagery (PRISMA and EnMAP) and high-resolution airborne measurements (HySpex) were harmonized to CHIME’s 30 m specifications utilizing Spectral Response Function (SRF) adjustments, Point Spread Function (PSF) spatial resampling, and 6S atmospheric radiative transfer modeling. For thermal data simulation, archive Landsat 8/9 and ASTER imagery were transformed into LSTM’s target 50 m, 5-band configuration using a synergistic two-step approach: a physics-based Spectral Super-Resolution (SSR) module followed by an AI-driven Spatial Super-Resolution (SpSR) transformer network. Evaluated across highly diverse inland, coastal, and riverine testbeds in Italy, the simulated products demonstrated high spectral, spatial, and radiometric fidelity. While inherently constrained by the native spectral ranges of the input sensors and by the current lack of absolute on-orbit mission data for validation, the downscaled images closely reproduced complex thermal patterns and water-quality gradients. Ultimately, this scalable framework provides the remote sensing community with early access to representative datasets and mission performance assessments, while accelerating pre-launch algorithm development and testing for environmental monitoring applications—particularly those focused on water discharges. Full article

Addressing the environmental challenges posed by the massive stockpiling of phosphogypsum (PG) has become a global concern, highlighting the urgency of developing large-scale, low-cost and resource-efficient utilization approaches for PG. This study was conducted in the rocky desertification areas of southwestern China, where land and water resources are scarce. Two land creation techniques—layered reconstruction (GA) and integrated construction (GB)—were adopted with modified PG to systematically investigate their impacts on soil properties and potato growth, yield and quality. The results showed that both techniques significantly improved soil conditions and enhanced potato yield and quality, with each presenting distinct characteristics in soil improvement. Specifically, the GA technique showed relatively better performance in soil nutrient enrichment, while the GB technique was more conducive to enhancing soil enzyme activity. Compared with the local red soil control, both techniques reduced heavy metal accumulation in potato tubers; however, Pb and Cd contents still exceeded national food safety limits, indicating potential food safety risks. In summary, land creation using modified PG can effectively increase arable land area, improve soil quality in rocky desertification regions, and simultaneously promote potato yield and quality. Nevertheless, as the current results are based on a single-season field trial, they cannot reflect the long-term patterns of heavy metal migration and accumulation. Therefore, for large-scale application, it is necessary to strengthen the monitoring of heavy metal levels in imported soil and long-term regional environmental impacts so as to ensure the quality and safety of agricultural products from reclaimed land. Full article

Water pollution control for wetland lakes has undergone a fluctuating development process. Effective pollution management requires not only scientific water quality monitoring data but also clear identification of pollution sources within the study area. Accordingly, this study investigated Mudong Lake, the core area of the Huixian Wetland, and conducted water quality monitoring in January 2023 (dry season) and June 2023 (wet season). Based on the Water Quality Index (WQI) assessment results, water quality was better in the wet season than in the dry season. To identify pollution sources, the Absolute Principal Component Score-Multiple Linear Regression (APCS-MLR) model was applied. The results showed that pollution in the dry season was mainly derived from aquaculture and agricultural non-point source pollution, anthropogenic point source pollution, and internal release from sediments, while pollution in the wet season exhibited mixed characteristics, driven by agricultural non-point sources, domestic sewage discharge, and natural factors. Source apportionment analysis indicated that composite pollution sources (domestic sewage and aquaculture wastewater), agricultural non-point source pollution, and other unidentified sources contributed 43.71%, 34.11%, and 22.18% of the total pollution load, respectively. The findings of this study can provide a scientific basis for pollution control, emission reduction, and the targeted management of Mudong Lake. Full article

The discharge of effluents from shrimp farms into coastal lagoons can alter food availability, water quality, and pollutant load, potentially affecting the biological performance and safety of farmed bivalves. This study evaluated the influence of shrimp farm effluents on the growth, total weight, and condition index of the Pacific oyster Crassostrea (Magallana) gigas. Two oyster cultivation zones were established in the coastal lagoon of Los Melagos (Sonora, Mexico): one near a shrimp effluent zone (EZ) and the other in a reference effluent-free zone (FZ). Shell height and length, total weight, and condition index were measured monthly, along with environmental variables, including chlorophyll “a” concentration and sea surface temperature obtained from satellite imagery. Oysters cultivated in EZ showed significantly higher total weight, condition index, and growth rates compared to those in FZ. Seasonal fluctuations in chlorophyll “a” were observed, reflected in growth patterns, suggesting greater food availability in waters influenced by effluents. However, these environments pose health risks that require continuous, integrated environmental and health monitoring. Full article

Freshwater ecosystems are increasingly threatened by eutrophication and other anthropogenic and climate-driven pressures that undermine ecological functioning and biodiversity. This study evaluates the transferability of a GIS-based multi-criteria decision analysis (GIS–MCDA) framework with Fuzzy Analytic Hierarchy Process (F-AHP), originally developed for a shallow coastal lake, to a morphologically distinct deep upland lake (Lough Tay, Ireland). Monthly in situ measurements at a single monitoring point in 2024 were analysed together with meteorological variables using Spearman rank correlations. Because spatial interpolation of in-lake water quality parameters was not feasible, eutrophication susceptibility was mapped using four external spatial drivers: distance from water resources (River Cloghoge inflows), land-based nitrogen export potential, distance from environmental pollutants represented by the transportation network, and a wind exposure index derived from a DEM and wind-rose analysis. Criteria were standardized with fuzzy membership functions, weighted using F-AHP (consistency index 0.056), and aggregated using weighted linear combination at 25 m resolution. The resulting Eutrophication Susceptibility Index (ESI) ranged from 0.18 to 0.81, indicating generally moderate to good conditions, with higher ESI values concentrated in the northern lake sector near inflow zones. The results demonstrate that GIS–MCDA can be adapted to lakes with limited monitoring by relying on external drivers, providing a spatial proxy for susceptibility rather than measured trophic status. Full article

The objective of this study was to assess and model the condition and resilience of the Cunas River using integrated indices and multivariate statistics in order to determine the impact of anthropogenic pressure and enhance water security in the Peruvian Andes. Stations in the upper, middle, and lower reaches of the river were monitored during the rainy and dry seasons, applying quality indices (NSF-WQI, WA-WQI, CCME-WQI, and I-WQI), principal component analysis (PCA), hierarchical cluster analysis (HCA), and Spearman’s rank correlation (ρ) to assess the intensity and direction of associations between physical–chemical parameters. The results reveal severe degradation in the lower section of the river, with critical hypoxia and extreme coliform levels during the dry season, drastically exceeding the levels in the upper reach. The I-WQI demonstrated superior performance (322.24; Unfit) by being more sensitive than the NSF-WQI (53.15–59.87). PCA confirmed that low flow explains the greatest variance in pollution (PC1 71.55%), while HCA identified maximum synergy (rescaling distance < 1) between biochemical oxygen demand (BOD₅) and total phosphorus, indicating the collapse of self-purification capacity. The HCA identified a maximum synergy between BOD₅ and total phosphorus during the low-flow season, while the PCA confirmed that low discharge intensifies pollutant concentrations. These findings support the need for resilience-based governance that prioritizes the protection of natural infrastructure. Full article

Studying zooplankton in freshwater ecosystems is crucial for ecological research, providing insight into ecosystem health, biodiversity, and water quality. This study focuses on developing a hybrid approach for studying and analyzing zooplankton communities using machine learning and human expert analysis. The goal of the study was to automate the labor-intensive process of zooplankton analysis as part of a long-term Lake Baikal monitoring program (since 1945), while maintaining continuity with traditional methods. A software and algorithmic system were developed to automate the analysis: images were processed using a two-stage pipeline (object detection using YOLO V11, classification using metric learning and visual transformers), and complex cases and new species were sent to specialists for verification. Over 240,000 images from 811 samples were processed, and models are updated using verified data to adapt to seasonal changes. An updatable database of labeled zooplankton images suitable for statistical analysis and research has been created. A comparison of manual and machine analysis revealed no significant differences in species composition, with accurate detection in 87% of images. This approach allows for scalable monitoring and the accumulation of labeled data arrays for the development of computer vision methods and the assessment of the state of Lake Baikal’s ecosystem. Full article

In response to the increasingly severe issue of heavy metal pollution in water, this paper proposes a method for the robust quantitative analysis of copper ions in purified water and real water samples based on the feature-level fusion of ultraviolet-visible absorption (UV-Vis) spectra and fluorescence spectra, combined with the Extreme Gradient Boosting (XGBoost) algorithm. Specifically, this study introduces a feature-level fusion strategy to overcome the limitations of single-spectrum detection, while the optimized XGBoost algorithm is employed to model the complex non-linear relationships that are difficult to capture using traditional linear regression methods. An optimization algorithm is introduced to fine-tune the model’s hyperparameters, thereby enhancing its predictive performance. Using the coefficient of determination (R²) and root mean square error (RMSE) as evaluation metrics, rapid and accurate detection of copper ions in water is achieved. Experimental results show that, for standard solutions, the optimized XGBoost model achieves a coefficient of determination of 0.9915 and a root mean square error of 2.6663 mg/L; for actual water samples, the optimized XGBoost model achieved a coefficient of determination of 0.9892 and RMSE of 1.2738 mg/L. This demonstrates the model’s strong generalization ability in overcoming the physical limitations of optical probes. This method effectively identifies and quantifies copper ions in water samples, demonstrating good accuracy and stability. Full article

The availability of high-resolution oceanographic data is critical for evidence-based coastal environmental management and climate resilience planning, yet it remains constrained by observational gaps and the prohibitive computational cost of fine-scale hydrodynamic modeling. While downscaling techniques provide a viable pathway, current data-driven approaches often lack statistical physical associations, overlook multivariate environmental interactions, and struggle to represent complex coastal topography. To address these limitations, we present MEOFGAN—an environmentally informed downscaling framework that integrates multivariate empirical orthogonal function (MEOF) decomposition with a generative adversarial network (GAN). The model extracts physically interpretable spatial modes of coupled ocean variables, learns their cross-scale transitions through adversarial training, and systematically incorporates high-resolution bathymetry as a static environmental constraint to enhance spatial fidelity. When applied to the Bohai Sea, MEOFGAN successfully downscales sea surface temperature (SST) and sea surface height (SSH) from 1/4° to 1/12°, achieving error reductions of 30–68% compared to benchmark methods while preserving ecologically relevant structural patterns (SSIM > 0.92). The framework demonstrates strong generalization by reconstructing 500 m resolution distributions of chlorophyll-a (Chl-a), dissolved oxygen (DO), and salinity in Bohai Bay, capturing fine-scale environmental gradients during a documented algal bloom event. This work establishes a methodological framework that can be transferred as a paradigm for generating high-resolution coastal datasets. Rather than serving as a universally transferable pre-trained model, the framework requires region-specific training and application. Data generated in this manner can directly support water quality monitoring, eutrophication assessment, habitat mapping, and regionally tailored climate adaptation strategies. Full article