Environmental Science and Pollution Research最新文献

Tokyo Electric Power Company Holdings began the oceanic release of treated water from the Fukushima Daiichi Nuclear Power Station (FDNPS) in August 2023, in which radioactive materials were effectively removed using the Advanced Liquid Processing System (ALPS). The environmental behavior of tritium, accounting for almost all radioactivity in ALPS-treated water, is of critical scientific and social concern. The accumulation possibility of tritium in marine organisms under the release conditions of ALPS-treated water was reviewed to ensure the safety of fishery products collected off the Fukushima coast and prevent unfounded reputation damages to the products. First, previous findings from actual measurements and numerical model estimations of the distribution of tritium derived from ALPS-treated water in seawater off the Fukushima coast are summarized to discuss the impact of oceanic release on tritium levels in seawater. As a result, the impact is suggested to be highly limited, which is indistinguishable from a natural level except for within 200 km from FDNPS. Second, the accumulation possibility of organically bound tritium (OBT) in marine organisms, such as phytoplankton, seaweed, and fish, was assessed using previous findings obtained from experimental and numerical studies, resulting in far smaller OBT accumulations in those organisms compared to the food-chain guideline proposed by FAO/WHO. Finally, the risks of internal exposure through the ingestion of fishery products collected off the Fukushima coast are discussed and quantitatively explained to be minimal especially in comparison with the food-chain guideline. However, continuous environmental monitoring of the oceanic release of ALPS-treated water is considered essential.

Heavy metal toxicity is a severe global threat, adversely affecting environmental health, food safety, and human well-being. Microbial biosorbents are a renowned effective solution for environmental pollution; however, their success depends on the adsorption capacity of the microbes. Metallothionein (MT) proteins are high in cysteine content and have a strong affinity to bind divalent metal ions. Thus, deliberately increasing cysteine content in bacterial MTs might hold promise in enhancing their metal-binding capacity. However, expressing these MTs in bacterial hosts is challenging due to many genetic constraints. In this study, we applied sequence-based protein design to generate three novel synthetic MT proteins (M_MT01, M_MT02, and M_MT03) with 32-38% cysteine content and enhanced metal ion binding potential. When expressed in Escherichia coli (E. coli) cytosol and on the cell surface, these MTs exhibited enhanced Cd/Cu tolerance and bioaccumulation capabilities. Notably, cytosolic expression of M_MT02 removed 153.6% more Cu and grew faster than the control, while M_MT03 enhanced Cd removal by 214.5%. However, cytotoxicity affected the growth rate. Moreover, the MT recombinant cells demonstrated antioxidant activity up to 81.7% higher than the control. To overcome intracellular metal toxicity, we further anchored M_MT02 and M_MT03 to the bacterial surface using the Lpp-OmpA (lipoprotein-outer membrane protein A) system to develop bacterial cell surface expression proteins that we called Lp-M_MT02 and Lp-M_MT03, accordingly. The Scanning Electron Microscopy-Energy Dispersive X-ray Spectroscopy (SEM-EDS) assay verified the expected cell surface expression and metal binding. Recombinant E. coli expressed Lp-M_MT02 and Lp-M_MT03 on their cell surface exhibited faster growth in Cd- and Cu-containing media than cells with only cytosolic expression. In fact, Lp-M_MT03 expression removed 46.4% more Cd from the media compared to M_MT03 expression. This study demonstrates the potential of de novo MT design technology and opens the door to utilizing the developed MTs for strain engineering to advance bio-absorption technologies.

Water quality monitoring is essential for managing and protecting surface water resources. Traditionally, assessing water quality relied on time-consuming laboratory analyses, which were prone to errors and often limited in accuracy, efficiency, and scalability. Conventional methods for calculating the water quality index (WQI) aggregate various water quality parameters into a single value to represent overall water quality. However, these traditional approaches often fail to capture the complex, nonlinear relationships between water quality parameters and their temporal and spatial variability, especially under fluctuating environmental conditions. Therefore, these limitations can lead to poor decision-making regarding overall water quality. Adopting reliable artificial intelligence (AI) methods for predicting the WQI is essential for achieving accurate predictions by leveraging computational processing to effectively approximate the WQI from complex, combined input variables. This approach enables the use of real-time monitoring and forecasting, which is crucial for implementing sophisticated, adaptive methodologies capable of handling large datasets and delivering more robust models for water quality assessment. This study investigates the efficacy of a one-dimensional convolutional neural network (1D-CNN) and support vector regression (SVR) in predicting the WQI of surface water. The WQI was first calculated analytically using the weighted arithmetic index method (WA-WQI) and used to assess the surface water quality at Tilesdit Dam in Bouira, Algeria. The performance of the prediction models was evaluated using three statistical metrics: the coefficient of determination (R²), root mean square error (RMSE), and mean absolute percentage error (MAPE), based on a 9-year dataset (2009-2018) of six key parameters from the study area. The 1D-CNN model demonstrated significantly higher performance metrics during both the training phase (R² = 0.9989, RMSE = 0.60, MAPE = 0.51) and the testing phase (R² = 0.9962, RMSE = 0.57, MAPE = 1.06), outperforming the SVR model, which showed lower performance in both phases: training (R² = 0.9597, RMSE = 3.61, MAPE = 0.57) and testing (R² = 0.976, RMSE = 1.68, MAPE = 3.01). Thus, the proposed approach to surface water quality assessment offers effective, adaptive, real-time solutions for advanced control strategies, resulting in more efficient water resource management in the study area. This method represents a significant advancement over conventional analytical techniques and supports proactive water resource management.

Due to a lack of understanding of plant-microbe-soil interaction, nitrogen (N) biogeochemistry in rice ecosystems remains a mystery. Specifically, data on temporal shifts in rhizospheric microbial communities under urea fertilization in irrigated rice systems are limited. This study investigated the temporal dynamics of bacterial community structure in the rhizosphere of urea-fertilized irrigated rice. The bacterial community exhibited higher species richness and evenness following the first and second dose of urea application but declined after the third dose. The results also revealed that soil N content had the most impact on the structures of the bacterial communities. The application of urea reduced bacterial families such as Sphingomonadaceae, Nocardioidaceae, and Kineosporiaceae, related to N fixation, organic matter decomposition, nutrient solubilization and methanogenesis, indicating their sensitivity to increased N levels. Conversely, Methylocystaceae, a methene oxidizing group, was increased after urea application suggesting their ability to proliferate under these conditions. Functional annotation using the KEGG pathway revealed elevated isoquinoline alkaloid biosynthesis after N applications. The findings of this study provide a basis for uncovering the bacterial community structure in the rice rhizosphere that is influenced by N fertilizer application.

Microplastic pollution affects all marine ecosystems, particularly coastal areas inhabited by sedentary reef-building organisms that rely on sand grains to build arenaceous reefs (e.g., Sabellariid polychaetes). These agglutinated reefs passively trap microplastics, thus increasing the potential risk to benthic organisms that live on and within the reef. An accurate quantitative assessment of microplastics accumulated within these arenaceous reefs is currently hindered by a lack of standardized methodologies. This study addresses this gap by developing and validating a reliable and reproducible protocol specifically designed to extract and quantify microplastics cemented within bioconstructed agglutinated matrices. The proposed protocol evaluated digestion procedures aimed at the release of microplastics from agglutinated matrices. The subsequent density extraction procedure was validated via a spiking experiment using both bioconstruction and sediment samples spiked with known quantities of polyethylene terephthalate, polypropylene, and polyvinyl chloride. Scanning electron microscopy and µ-Raman spectroscopy confirmed that the adopted digestion procedures did not alter the plastic polymers. Results also showed that the NaI solution yielded a significantly higher microplastic recovery than NaCl. Notably, microplastic recovery using NaCl was influenced by the initial sample weight, suggesting possible matrix interference at higher sample weights. Our multistep approach provides a validated, cost-effective, and reproducible protocol that improves microplastic quantification in agglutinated matrices. By employing common laboratory equipment and specific procedures, this methodology represents a significant step towards standardizing microplastic pollution monitoring in coastal bioengineered habitats. HIGHLIGHTS: • A step-by-step protocol for MP density extraction from biogenic agglutinated matrices was validated. • Agglutinated matrices require disaggregation to release MP, ensuring a correct density extraction. • Preliminary drying and disaggregation procedures do not alter the chemical integrity of MP. • NaI solution is significantly more efficient than NaCl for MP extraction. • Substrate type (sediment vs Sabellariid bioconstruction) had no influence on  density extraction efficiency.

Visible light-responsive CdFe₂O₄ photocatalysts were synthesized via a one-step combustion route using a series of structurally distinct carboxylic acid-based fuels, enabling deliberate modulation of nanostructure and optoelectronic properties. The functional groups and molecular architecture of the fuels played a crucial role in governing combustion behavior, which in turn dictated crystallite growth, surface morphology, and bandgap tuning in the resulting CdFe₂O₄ nanoparticles. These tailored photocatalysts were evaluated for malachite green (MG) degradation in a Vis-light/CdFe₂O₄/H₂O₂ system, exhibiting pseudo-first-order kinetics with apparent rate constants enhanced by up to 31-fold as a function of solution pH. MG degradation proceeded via reactive oxygen species (ROS), including h⁺, ^·OH, and O₂⁻^·, with h⁺ identified as the dominant oxidative species. The system showed strong pH responsiveness and sensitivity to inorganic salts ions, attributable to fuel-dependent adjustments in surface charge and morphology. Furthermore, the CdFe₂O₄ displayed excellent stability, retaining high photocatalytic activity over nine consecutive reuse cycles.

The complex mixtures known as hydrocarbons (HCs) are primarily made up of saturates, aromatics, resins, and asphaltenes, which are collectively called SARA fractions. These components, which differ based on the source, make up a significant amount of crude oil. Coastal waters are especially affected by the discharge of hydrocarbons into marine environments as a result of human-caused activities like industrial effluents, maritime traffic, and coastal tourism. The coastal area of Goa is becoming more susceptible to hydrocarbon contamination because of its ecological sensitivity and industrial pressures. In this review, the chemical, physical, and biological degradation mechanisms that control the fate of hydrocarbons in Goa's coastal waters are critically evaluated. With a focus on studies published between 2015 and 2025, a systematic literature review was done using databases like Scopus, Web of Science, and Google Scholar. According to the review, the primary natural attenuation process is microbial degradation, which is greatly impacted by temperature, salinity, oxygen concentration, and nutrient availability. TPH concentrations in Goa's coastal waters are reported to vary greatly. Significant knowledge gaps still exist, especially with regard to in-situ bioremediation trials, microbial community profiling, and site-specific monitoring data. The review emphasizes the need for focused mitigation strategies adapted to Goa's environmental and socioeconomic context, as well as the absence of integrated monitoring frameworks. The results provide a scientific foundation for better environmental management, the creation of policies, and long-term remediation techniques in coastal areas affected by hydrocarbons.

This study developed a synergistic treatment system based on nano-ZnFe₂O₄ activated peroxymonosulfate (PMS), which can realize efficient simultaneous removal of refractory P(III)-Ni(II) complexes from electroless nickel plating wastewater via a surface-confined oxidation-adsorption mechanism. Isotherm experiments at 25 ℃ and pH 6 demonstrated that the ZnFe₂O₄/PMS/P(III)-Ni(II) system achieved maximum adsorption capacities of 0.56 mmol/g for total phosphorus (TP) and 0.31 mmol/g for Ni, as derived from the Sips and Langmuir models, respectively. Kinetic analysis revealed that TP removal followed pseudo-second-order kinetics, whereas Ni removal adhered to pseudo-first-order kinetics. The introduction of PMS significantly enhanced adsorption capacity, with the equilibrium TP adsorption in the ZnFe₂O₄/PMS/P(III) system increasing by 109.1% and 64.3% compared to the ZnFe₂O₄/P(III) and ZnFe₂O₄/P(V) systems, respectively. Ni(II) participated in PMS activation to generate Ni(III), resulting in a 60.9% higher TP removal rate in the ZnFe₂O₄/PMS/P(III)-Ni(II) system than in the ZnFe₂O₄/PMS/P(III) system. Quenching experiments identified singlet oxygen as the dominant reactive oxygen species, which oxidized P(III) to P(V) via a non-radical pathway. XPS further confirmed the in situ immobilization of P(V) through M-O-P bonding. The phosphorus removal exhibited strong anti-interference capability, maintaining high TP removal efficiency across a wide pH range (3-9) and in the presence of coexisting ions. ZnFe₂O₄ retained over 92% of the TP removal after five cycles in the recycling tests. This study provides a novel strategy for advanced treatment of complex electroplating wastewater.

Groundwater resource utilization in many developing countries, including Ethiopia, is significantly constrained by limited information on its quality and quantity, often due to challenges associated with geophysical and hydrogeological assessments. In recent years, geographic information system (GIS) and remote sensing (RS) technologies have emerged as valuable tools for understanding the spatial distribution of groundwater resources, aiding in their planning, exploration, monitoring, and management. Thus, this study aims to delineate potential groundwater availability zones in the Angereb Watershed, located in northwestern Ethiopia, using a geospatial approach integrated with multi-criteria evaluation (MCE) and the analytical hierarchical process (AHP) model. Multiple thematic layers were prepared from various data sources, including Landsat 8 OLI, Shuttle Radar Topography Mission (SRTM), geological maps, soil, and rainfall data. Key factors influencing groundwater availability namely drainage density, lineament density, lithology, slope, soil type, mean annual rainfall, and Normalized Difference Vegetation Index (NDVI) were selected and weighted using the AHP model within ArcGIS 10.3. The analysis identified lithology, lineament density, slope, and drainage density as dominant factors, collectively accounting for approximately 85.3% of the influence on groundwater potential. The final groundwater potential index (GWPI) map categorized the study area into four zones: very high (0.1%; 546 ha), high (12.5%; 93,404 ha), medium (79.4%; 592,302 ha), and low (8%; 59,818 ha) potential. The high-potential zones were primarily located in the northwestern and southern regions, influenced largely by favorable geological and physiographic conditions. The predictive performance of the GWPI map was validated using the receiver operating characteristic (ROC) curve and area under the curve (AUC) analysis, yielding an AUC value of 0.83, indicating strong model reliability. This study demonstrates that the integration of MCE with GIS and remote sensing techniques, supported by AHP, offers a cost-effective and reliable method for delineating groundwater potential zones and can serve as a valuable tool for groundwater management and planning in similar data-scarce environments.