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Antioxidant system alterations and oxidative stress caused by polyfluoroalkyl substances (PFAS) in exposed biota: a review

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science of the Total Environment 2025年 977卷 179395页

作者： Soares, Lorena Oliveira Souza de Araujo, Gabriel Farias Gomes, Thais Braga Júnior, Sidney Fernandes Sales Cuprys, Agnieszka Katarzyna Soares, Raquel Moraes Saggioro, Enrico Mendes Laboratory of Environmental Health Assessment and Promotion Oswaldo Cruz Institute Oswaldo Cruz Foundation Av. Brasil 4365 RJ Rio de Janeiro21045-900 Brazil Post-Graduate Program in Public Health and Environment Sergio Arouca National School of Public Health Oswaldo Cruz Foundation Av. Leopoldo Bulhões 1480 RJ Rio de Janeiro21041-210 Brazil Faculty of Science and Technology Norwegian University of Life Sciences P.O. Box 5003 Ås1432 Norway Post-Graduate Program in Environmental Technology and Water Resources Department of Civil and Environmental Engineering – FT University of Brasília Darcy Ribeiro Campus Via L3 Norte DF Brasília70910-900 Brazil

Contamination of aquatic and terrestrial organisms by Perfluoroalkyl substances (PFAS), emerging contaminants, is widespread, as these compounds are present in water, soil, air, and food, owing to their environmental persistence. PFAS exposure induces biochemical process alterations associated with the disruption of the antioxidant defense system in several species. This review aims to discuss how PFAS-induced antioxidant system alterations lead to changes in biochemical processes in different organisms exposed to these pollutants. This disruption, then leads to an imbalance in antioxidant defense systems, contributing to the formation of reactive oxidative species (ROS), which, in turn, can be exacerbate oxidative stress, induce cellular damage, enhance lipid peroxidation, destabilize lysosomal membranes, and cause genotoxic effects, ultimately compromising DNA integrity. In acute tests, PFAS have led to mortality, growth inhibition, diminished behavioral and locomotor abilities, and reproductive impairment. PFAS-induced effects differ with varying species or types of substances, and further bioaccumulation through food chains exacerbates environmental contamination, carrying considerable risks. These findings demonstrate the complex and enduring impact of PFAS on environmental health, emphasizing the importance of this review in corroborating studies on sub-lethal toxicity in exposed organisms and how these effects reflect on the environment. © 2025 Elsevier B.V.

关键词： Biotic

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Evaluation of reusing alum sludge for the coagulation of industrial wastewater containing mixed anionic surfactants

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Journal of environmental sciences 2011年第4期23卷 587-594页

作者： Siriprapha Jangkorn Sinchai Kuhakaew Suwapee Theantanoo Harit Klinla-or Tongchai Sriwiriyarat Environmental Science Program Faculty of Science Burapha University Department of Chemical Engineering Faculty of Engineering Burapha University

A coagulation-flocculation process is typically employed to treat the industrial wastewater generated by the consumer products industry manufacturing detergents, soaps, and others. The expenditure of chemicals including coagulants and chemicals for pH adjustment is costly for treating this wastewater. The objective of this study was to evaluate the feasibility of reusing the aluminum sulfate (alum) sludge as a coagulant or as a coagulation aid so that the fresh alum dosage can be minimized or the removal efficiency can be enhanced. The experiments were conducted in a jar-test apparatus simulating the coagulation-flocculation process for simultaneous removals of organic matters, anionic surfactants, suspended solids, and turbidity. At the optimum initial pH value of 10 and the fresh alum concentration of 400 mg/L, the total suspended solids (TSS), total chemical oxygen demand (TCOD), total anionic surfactants, and turbidity removal efficiencies were 71.5%, 76.4%, 95.4%, and 98.2%, respectively. The addition of alum sludge as a coagulant alone without any fresh alum addition could significantly remove the turbidity, TCOD, and anionic surfactants. The TSS was left in the supernatants after the settling period, but would subsequently be removed by adding the fresh alum. The TSS, TCOD, and turbidity removal efficiencies were also enhanced when both the alum sludge and the fresh alum were employed. The TCOD removal efficiency over 80% has been accomplished, which has never fulfilled by using the fresh alum alone. It is concluded that the alum sludge could be reused for the treatment of industrial wastewater generated by the consumer products industry.

关键词： alum sludge coagulation anionic surfactants consumer products

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A multi-nutrient nanocomposite enhances UV stress tolerance and modulates nutrient accumulation in lettuce

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environmental science: Nano 2025年

作者： Thangavelu, Raja muthuramalingam da Silva, Washington Hernandez-Viezcas, Jose A. Oyanedel-Craver, Vinka Gardea-Torresdey, Jorge L. Dimkpa, Christian O. White, Jason C. Mena, Nubia Zuverza Connecticut Agricultural Experiment Station New Haven 06511 CT United States Department of Chemistry and Biochemistry The University of Texas at El Paso 500 West University Avenue El Paso 79968 TX United States Department of Civil and Environmental Engineering University of Rhode Island Kingston RI United States Environmental Science and Engineering PhD Program University of Texas at El Paso El Paso United States

This study introduces a novel multielement (Zn-Mg-Mn-Fe) nanocomposite that serves both as a UV-protective agent and a nutrient delivery system for Lactuca sativa (lettuce). Plants were grown indoors in a potting soil-like mix, under artificial lighting (from light emitting diodes, LEDs) or under LED + UV radiation to simulate excessive sunlight exposure (light stress). Lettuce was treated with foliar applications of the nanocomposite at 100 mg L⁻¹, 200 mg L⁻¹, and 300 mg L⁻¹, with 1 mL applied per plant during the fifth week of growth (a total of 0.1, 0.2 or 0.3 mg of the composite respectively per plant). Plants were exposed to UV radiation (360-400 nm) for 10 hours daily over two weeks. The 300 mg L⁻¹ treatment significantly enhanced photosynthetic efficiency and plant growth, increasing chlorophyll content (66.7% ± 3.5), leaf area (45% ± 2.1), and dry biomass (43.68% ± 1.8) compared to untreated and ionic controls. It also mitigated UV-induced stress, reducing UV-induced damage scores by 73% compared to controls and lowering stress markers, with flavonoid production reduced to 30.5% ± 2.3 of control levels and SOD activity reduced to 25.8% ± 1.8 of control levels. The composite's-controlled nutrient release mechanism facilitated rapid Mg uptake (220 mg kg⁻¹ dry weight in leaves within 4 days) and sustained delivery of Zn, Mn, and Fe over a 7-10-day period. Long-term nutrient uptake analysis showed increases in Mn (55.3% ± 3.2), Mg (47.8% ± 2.7), and Fe (62.5% ± 4.1). Enhanced P (28.5% ± 2.2) and K (35.7% ± 3.1) accumulation further boosted the nutritional quality of edible tissues. Additionally, the nanocomposite demonstrated the unique ability to convert harmful UV radiation into visible light, providing dual benefits of UV protection and enhanced photosynthetic activity. These findings highlight the potential of this multi-functional nanocomposite as a sustainable solution to improve crop resilience, optim

关键词： Lettuce

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A preliminary data fusion study to assess the feasibility of Foundation Process-Property Models in Laser Powder Bed Fusion

arXiv

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arXiv 2025年

作者： Vendrell-Gallart, Oriol Negarandeh, Nima Foumani, Zahra Zanjani Amiri, Mahsa Valdevit, Lorenzo Bostanabad, Ramin Department of Mechanical and Aerospace Engineering University of California IrvineCA92697 United States Materials and Manufacturing Technology Program University of California IrvineCA92697 United States Department of Materials Science and Engineering University of California IrvineCA92697 United States Department of Civil and Environmental Engineering University of California IrvineCA92697 United States

Foundation models are at the forefront of an increasing number of critical applications. In regards to technologies such as additive manufacturing (AM), these models have the potential to dramatically accelerate process optimization and, in turn, design of next generation materials. A major challenge that impedes the construction of foundation process-property models is data scarcity. To understand the impact of this challenge, and since foundation models rely on data fusion, in this work we conduct controlled experiments where we focus on the transferability of information across different material systems and properties. More specifically, we generate experimental datasets from 17-4 PH and 316L stainless steels (SSs) in Laser Powder Bed Fusion (LPBF) where we measure the effect of five process parameters on porosity and hardness. We then leverage Gaussian processes (GPs) for process-property modeling in various configurations to test if knowledge about one material system or property can be leveraged to build more accurate machine learning models for other material systems or properties. Through extensive cross-validation studies and probing the GPs’ interpretable hyperparameters, we study the intricate relation among data size and dimensionality, complexity of the process-property relations, noise, and characteristics of machine learning models. Our findings highlight the need for structured learning approaches that incorporate domain knowledge in building foundation process-property models rather than relying on uninformed data fusion in data-limited applications. © 2025, CC BY.

关键词： Data fusion

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Projecting Forest Fire Probability in South Korea under Climate Change, Population, and Forest Management Scenarios Using AI & Process-Based Hybrid Model (FLAM-Net)

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 13003-13022页

作者： Jo, Hyun-Woo Won, Myoung-Soo Kraxner, Florian Jeon, Seongwoo Son, Yowhan Krasovskiy, Andrey Lee, Woo-Kyun Seoul20841 Korea Republic of Group Laxenburg Austria National Institute of Forest Science National Forest Satellite Information & Technology Center Seoul05203 Korea Republic of Program Laxenburg Austria Korea University Department of Environmental Science and Ecological Engineering Seoul20841 Korea Republic of

Climate change-induced heat waves and densely forested areas near urban centers in South Korea create complex challenges for wildfire response systems. Various forest fire models have been developed to address this, each with unique strengths and weaknesses. Process-based models offer high interpretability through human domain knowledge but require extensive optimization, while machine learning models automatically identify important features but have limited interpretability. To leverage the strengths of both models, this study aimed to integrate human domain knowledge into a machine learning framework. IIASA's wildfire cLimate impacts and Adaptation Model (FLAM)—a process-based model incorporating biophysical and human impacts—was developed as a neural network called FLAM-Net. Enhancements included improving backpropagation for optimization and introducing algorithms for national-specific fire ignition dynamics. FLAM-Net was applied at multiple scales and integrated through U-Net-based architecture, named Deep Neural FLAM (DN-FLAM), to produce downscaled predictions. The optimization revealed spatial concentration of fires near metropolitan areas and the east coast, with temporal concentration in spring due to agricultural burning. Integration of multi-scale features through DN-FLAM achieved optimal performance with Pearson's r values of 0.943, 0.840, and 0.641 for temporal, spatial, and spatio-temporal validations. Future projections based on Shared Socioeconomic Pathways (SSP) indicated increasing fire frequencies until 2050, followed by a decrease due to increased precipitation. This study demonstrates the benefits of the hybrid approach, providing interpretability, accuracy, and efficient optimization. These hybrid models offer scientific evidence to guide locally tailored decision-making for climate change-induced forest fires and lay the groundwork for global application through their optimization capabilities. © 2008-2012 IEEE.

关键词： Global warming

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Machine learning-based analysis on factors influencing blood heavy metal concentrations in the Korean CHildren's environmental health Study (Ko-CHENS)

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science of the Total Environment 2025年 978卷

作者： Jung, Seowoo Shah, Surabhi Oh, Jongmin Bang, Yoorim Lee, Ji Hyen Kim, Hwan-Cheol Jeong, Kyoung Sook Park, Huibyeol Lee, Eun-Kyung Hong, Yun-Chul Ha, Eunhee Department of Statistics Ewha Womans University Seoul Korea Republic of Department of Environmental Medicine College of Medicine Ewha Womans University Seoul Korea Republic of Department of Human Systems Medicine College of Medicine Seoul National University Seoul Korea Republic of College of Medicine Ewha Womans University Seoul Korea Republic of Institute for Development and Human Security Ewha Womans University Seoul Korea Republic of Department of Pediatrics College of Medicine Ewha Womans University Seoul Korea Republic of Department of Occupational and Environmental Medicine College of Medicine Inha University Hospital Inha University Incheon Korea Republic of Department of Occupational and Environmental Medicine Wonju College of Medicine Wonju Severance Christian Hospital Yonsei University Wonju Korea Republic of Environmental Health Research Division National Institute of Environmental Research Incheon Korea Republic of Institute of Environmental Medicine Medical Research Center Seoul National University Korea Republic of Graduate Program in System Health Science and Engineering Ewha Womans University Ewha Medical Research Institute College of Medicine Seoul Korea Republic of

Heavy metal concentration in pregnant women affects neurocognitive and behavioral development of their infants and children. The majority of existing research focusing on pregnant women's heavy metal concentration has considered individual environmental factor. In this study, we aim to comprehensively consider lifestyle, food, and environmental factors to determine the most influential factor affecting heavy metal concentration in pregnant women. The Ko-CHENS (Korean CHildren health and environmental Study) is a nationwide prospective birth cohort study in South Korea enrolling pregnant women from 2015 to 2020. A total of 5458 eligible pregnant women were included in this study, and 897 variables were included in questionnaire comprising: maternal general information, indoor and living environment, dietary habits, health behavior, exposure to chemicals. Lead, cadmium and mercury concentration on blood were measured in early, late pregnancy and in cord blood at birth. Variables that might be related to heavy metal concentrations were included in machine learning models. Random forest and XGBoost machine learning models were conducted for predictions. Both models had similar but better performance than multiple linear regression. Kimchi (β = 1.55), seaweed (β = 0.40), fatty fish (β = 1.55), intakes respectively affected lead, cadmium, and mercury exposure through early, late pregnancy and cord blood. © 2025

关键词： Obstetrics

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Novel continuous single-step synthesis of nitrogen-modified TiO_2 by flame spray pyrolysis for photocatalytic degradation of phenol in visible light

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Journal of Materials science & Technology 2018年第9期34卷 1494-1502页

作者： Thirupathi Boningari Siva Nagi Reddy Inturi Makram Suidan Panagiotis G.Smirniotis Chemical Engineering Program College of Engineering & Applied Science University of Cincinnati Environmental Engineering College of Engineering & Applied Science University of Cincinnati

A novel rapid and continuous process has developed for the synthesis of nitrogen-doped TiO2（N-TiO2）with flame spray pyrolysis（FSP） method. The nitrogen incorporation into TiO2 was achieved by a facile modification（addition of dilute nitric acid） in the precursor for the synthesis. The catalysts were characterized by X-ray diffraction, Raman spectroscopy, transmission electron microscopy, diffuse reflectance spectroscopy, and X-ray photoelectron spectroscopy. The doping of nitrogen into the TiO2 was confirmed by X-ray photoelectron spectroscopy（XPS） and energy dispersive X-ray（EDX） spectroscopy. The UV-vis spectra of the modified catalysts（with primary N source） exhibited band-gap narrowing for 4 N-TiO2 with band gap energy of 2.89 eV, which may be due to the presence of nitrogen in TiO2 structure. The introduction of secondary N-source（urea） into TiO2 crystal lattice results in additional reduction of the band gap energy to 2.68 eV and shows a significant improvement of visible light absorption. The N-TiO2 nanoparticles modified by using secondary N-source showed significant photocatalytic activity under visible light much higher than TiO2. The higher activity is attributed to the synergetic interaction of nitrogen with the TiO2 lattice. The lowering of the band-gap energy for the flame made N-doped TiO2 materials implies that the nitrogen doping in TiO2 by aerosol method is highly effective in extending the optical response of TiO2 in the visible region. The nitrogen atomic percentage has increased monotonically（0.09%-0.15%）with the increase in primary nitrogen source（nitric acid）, and significantly boosted to 0.97% when secondary nitrogen source（urea） was introduced. The highest rate of phenol degradation was obtained for catalysts with secondary N source due to increase in N content in the catalyst.

关键词： Flame spray pyrolysis（FSP） Titania （TiO2） Visible-light-induced Liquid phase Phenol photodegradation

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Multistate Control of Nonlinear Photocurrents in Optoferroelectrics via phase manipulation of light field

arXiv

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arXiv 2025年

作者： Kazempour, Ali Sisakht, Esmaeil Taghizadeh Okyay, Mahmut Sait Jiang, Xiao Sato, Shunsuke Park, Noejung Department of physics Payame Noor University PO BOX Tehran119395-3697 Iran Department of Physics Ulsan National Institute of Science and Technology Ulsan689-798 Korea Republic of Materials Science and Engineering Program Department of Chemical and Environmental Engineering University of California-Riverside RiversideCA92521 United States Center for Computational Sciences University of Tsukuba Tsukuba305-8577 Japan

Ultrafast optical control of ferroelectricity based on short and intense light can be utilized to achieve accurate manipulations of ferroelectric materials, which may pave a basis for future breakthrough in nonvolatile memories. Here, we demonstrate that phase manipulation of electric field in the strong field sub-cycle regime induces a nonlinear injection current, efficiently coupling with the topology of band structure and enabling dynamic reversal of both current and polarization. Our time-dependent first-principles calculations reveal that tuning the phase of linearly or circularly polarized light through time-varying chirp, or constant carrier envelop phases within sub-laser-cycle dynamics effectively breaks the time-reversal symmetry, allowing the control over current and electronic polarization reversal over multi-ferroelectric states. Our time- and momentum-resolved transverse current analysis reveal the significance of Berry curvature higher order poles in the apparent association between the odd (even) orders of Berry curvature multipoles to odd (even) pseudo-harmonics in driving polarization dynamics reversal. We suggest that these phase manipulations of short pulse waveform may lead to unprecedented accurate control of nonlinear photocurrents and polarization states, which facilitate the development of precise ultrafast opto-ferroelectric devices. © 2025, CC BY.

关键词： Ferroelectricity

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Unveiling Microplastics Abundance and Mangrove Reactions: A Comprehensive Study in the Pekalongan Mangrove Area

SSRN

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SSRN 2025年

作者： Ismanto, Aris Zainuri, Muhammad Ismunarti, Dwi Haryo Hadibarata, Tony Safinatunnajah, Novia Rahmansyah, Raihan Anandra Sugianto, Denny Nugroho Rochaddi, Baskoro Kristanti, Risky Ayu Hernawan, Undang Purbani, Dini Department of Oceanography Faculty of Fisheries and Marine Science Universitas Diponegoro Semarang50275 Indonesia Environmental Engineering Program Faculty of Engineering and Science Curtin University Malaysia Miri CDT 250 Malaysia Department of Marine Science Faculty of Fisheries and Marine Science Universitas Diponegoro Semarang50275 Indonesia Research Center for Oceanography National Research and Innovation Agency Jakarta14430 Indonesia Research Center for Conservation of Marine Resources and Inland Waters Cibinong16911 Indonesia

Mangrove ecosystem in Pekalongan city provides significant ecological functions, biodiversity hot spots, economic and social benefits, ecosystem services, coastal protection, and sources of livelihood for local people in Central Java. Currently, this mangrove ecosystem is undergoing significant degradation, primarily due to anthropogenic activities such as waste from the batik industry, textile production, and residential areas., Mangroves, which are a fence between land and sea, are very likely to be exposed to microplastic waste. Waters and sediment samples were analyzed to find the microplastics at mangrove area. The analyzed of shape, size and colors using Microscope Motic SMZ-161 equipped with camera and the functional group of the polymers were analyzed using ATR-FTIR. From this research the microplastics abundance were 109 particles/l from total of all research stations in the water column and 153 particles/kg from total of all research stations in the sediment. The functional group polymer identified the polyamide, polystyrene, rayon, polyethylene terephthalate, polyethylene, and polyester at this research area. The anthropogenic activities in this area will increase waste in the form of microplastics and other chemicals, which will end up in marine waters and can have an impact on the mangrove growth and health. © 2025, The Authors. All rights reserved.

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Harnessing machine learning for transmembrane pressure prediction in MBR systems during textile wastewater treatment

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Desalination and Water Treatment 2025年 322卷

作者： Zahoor, Onaira Khan, Sher Jamal Usama, Muhammad Tanudjaja, Henry J. Ghaffour, Noreddine Nawaz, Muhammad Saqib Institute of Environmental Sciences and Engineering (IESE) School of Civil and Environmental Engineering (SCEE) National University of Sciences and Technology (NUST) Sector H-12 Islamabad Pakistan Department of Civil and Environmental Engineering Northeastern University Boston 02115 MA United States Environmental Science and Engineering Program Division of Biological and Environmental Science and Engineering (BESE) King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia

Membrane Bioreactors (MBRs) are preferred for treating high-strength industrial wastewater due to their exceptional treatment efficiency and compact design. However, membrane fouling remains a significant challenge that limits their broader application. Since transmembrane pressure (TMP) is closely associated with membrane fouling mechanisms, its accurate prediction is essential for anticipating fouling events. This study focuses on predicting TMP in a bench-scale MBR by employing advanced regression models such as Lasso, support vector machines, and random forest. The bioreactor is operated using synthetic textile wastewater, with a primary focus on critical sludge parameters. A total of 60 datasets were compiled for training and testing of the models. The random forest model attained an R² of 0.95 and 0.86 and root mean square error values of 1.75 kPa and 3.3 kPa for the training and test datasets, respectively, demonstrating the best predictive accuracy. Extra polymeric substances were the most influential parameter, while particle size distribution was the least. The random forest and lasso regression models predicted high values more accurately, while the support vector regression model was inclined towards making more conservative predictions for extreme values. The effectiveness of the proposed models showcases the significance of utilizing machine learning in advancing MBR technology for wastewater treatment. Accurate TMP predictions through machine learning can help in maintaining key parameters within optimal ranges, thereby reducing the likelihood of membrane fouling and enhancing system efficiency. © 2025 The Authors

关键词： Extra polymeric substances Machine learning models Membrane bioreactor Textile wastewater treatment Transmembrane pressure

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