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Search Results (247)

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16 pages, 1416 KiB  
Article
Association of Personal Care and Consumer Product Chemicals with Long-Term Amenorrhea: Insights into Serum Globulin and STAT3
by Ziyi Li, Xue Song, Daniel Abdul Karim Turay, Yanling Chen, Guohong Zhao, Yingtong Jiang, Kun Zhou, Xiaoming Ji, Xiaoling Zhang and Minjian Chen
Toxics 2025, 13(3), 187; https://doi.org/10.3390/toxics13030187 - 5 Mar 2025
Viewed by 230
Abstract
Chemicals in personal care and consumer products are suspected to disrupt endocrine function and affect reproductive health. However, the link between mixed exposure and long-term amenorrhea is not well understood. This study analyzed data from 684 women (2013–2018 National Health and Nutrition Examination [...] Read more.
Chemicals in personal care and consumer products are suspected to disrupt endocrine function and affect reproductive health. However, the link between mixed exposure and long-term amenorrhea is not well understood. This study analyzed data from 684 women (2013–2018 National Health and Nutrition Examination Survey) to assess exposure to eight polyfluorinated alkyl substances (PFASs), 15 phthalates (PAEs), six phenols, and four parabens. Various statistical models for robustness tests and mediation analysis were used to explore associations with long-term amenorrhea and the role of serum globulin. Biological mechanisms were identified through an integrated strategy involving target analysis of key chemicals and long-term amenorrhea intersections, pathway analysis, and target validation. Results showed that women with long-term amenorrhea had higher exposure levels of Perfluorodecanoic acid, Perfluorohexane sulfonic acid (PFHxS), Perfluorononanoic acid, n-perfluorooctanoic acid (n_PFOA), n-perfluorooctane sulfonic acid, and Perfluoromethylheptane sulfonic acid isomers. Logistic regression with different adjustments consistently found significant associations between elevated PFAS concentrations and increased long-term amenorrhea risk, confirmed by Partial Least Squares Discriminant Analysis. Mediation analysis revealed that serum globulin partially mediated the relationship between PFAS exposure and long-term amenorrhea. Network and target analysis suggested that PFHxS and n_PFOA may interact with Signal Transducer and Activator of Transcription 3 (STAT3). This study highlights significant associations between PFAS exposure, particularly PFHxS and n_PFOA, and long-term amenorrhea, with serum globulin and STAT3 serving as mediators in the underlying mechanisms. Full article
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<p>Flow chart of the screening process from NHANES (2013–2018).</p>
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<p>Identification of common targets, interaction networks, and target validation between PFHxS, n_PFOA, and long-term amenorrhea. (<b>A</b>) Venn diagram of common targets between PFHxS, n_PFOA, and long-term amenorrhea. (<b>B</b>,<b>C</b>) PPI network for PFHxS, n_PFOA, and long-term amenorrhea targets. (<b>D</b>,<b>E</b>) KEGG and GO pathway analyses of PFHxS and n_PFOA targets in long-term amenorrhea. (<b>F</b>,<b>G</b>) Molecular docking analysis of PFHxS and n_PFOA binding to STAT3 protein.</p>
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24 pages, 4379 KiB  
Article
KOH-Assisted Chemical Activation of Camelina Meal (Wild Flax) to Treat PFOA-Contaminated Wastewater
by Shivangi Jha, Falguni Pattnaik, Oscar Zapata, Bishnu Acharya and Ajay K. Dalai
Sustainability 2025, 17(5), 2170; https://doi.org/10.3390/su17052170 - 3 Mar 2025
Viewed by 352
Abstract
This study is constituted of the chemical activation of camelina meal (CM) biochar and the utilization of these activated carbon for the adsorption of perfluorooctanoic acid (PFOA) from water. Camelina meal, a sustainable agro-based byproduct, underwent slow pyrolysis and subsequent chemical activation with [...] Read more.
This study is constituted of the chemical activation of camelina meal (CM) biochar and the utilization of these activated carbon for the adsorption of perfluorooctanoic acid (PFOA) from water. Camelina meal, a sustainable agro-based byproduct, underwent slow pyrolysis and subsequent chemical activation with potassium carbonate (K2CO3), potassium hydroxide (KOH), and sodium hydroxide (NaOH). Among these chemical activating agents, KOH emerged as the one of most efficient activating agents, yielding activated carbon with superior surface properties and significantly higher carbon content. After the screening of the activating agents, a central composite design (CCD) was employed to optimize the critical constraints like temperature (600–900 °C), activation time (60–120 min), and KOH-to-feed ratio (0.5–1.5), with the objective of maximizing the surface area and adsorption capacities of the activated carbon samples. The activated carbon exhibited a substantial enhancement in surface area and PFOA adsorption efficacy. Optimal adsorption of PFOA was achieved using activated carbon produced at 800 °C with an activation time of 60 min and a KOH-to-feed ratio of 1.5. This material exhibited a surface area of 1558.4 m2/g and demonstrated a PFOA removal efficiency of 92.3%. The findings underscore the efficacy of chemically activated camelina meal biochar as an ecological adsorbent for the remediation of PFOA-polluted water. Full article
(This article belongs to the Section Sustainable Chemical Engineering and Technology)
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<p>Graphical comparison between experimental and predicted values of surface area (m<sup>2</sup>/g) for activated KOH biochar.</p>
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<p>The interaction between temperature and time and their effects on the surface area of the activated carbon (The red dots are the data points above the plot whereas the pink dots are the data points below the plot).</p>
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<p>The interaction between temperature and chemical-to-feed ratio and their effects on the surface area of the activated carbon (The red dots are the data points above the plot whereas the pink dots are the data points below the plot).</p>
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<p>The interaction between time and chemical-to-feed ratio and their effects on the surface area of the activated carbon (The red dots are the data points above the plot whereas the pink dots are the data points below the plot).</p>
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<p>The optimized conditions and predicted response for the chemical activation of the camelina meal biochar.</p>
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<p>FTIR spectra of the activated carbon showing significantly substantial results in the adsorption experiments.</p>
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<p>NMR spectra of the activated carbon showing significantly substantial results in the adsorption experiments.</p>
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<p>SEM micrographs revealing the morphology of (<b>a</b>) camelina meal biomass, (<b>b</b>) camelina meal biochar prepared at 450 °C, (<b>c</b>) KOH-activated camelina meal biochar, and (<b>d</b>) KOH-activated camelina meal biochar after PFOA adsorption.</p>
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<p>Effect of adsorbent dosage on PFOA removal efficiency of KOH-activated carbon and CM biochar.</p>
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<p>Effect of time on (<b>a</b>) PFOA concentration and (<b>b</b>) removal efficiency of KOH-activated carbon and CM Biochar.</p>
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<p>Effect of time on (<b>a</b>) PFOA concentration and (<b>b</b>) removal efficiency of KOH-activated carbon and CM Biochar.</p>
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11 pages, 1807 KiB  
Communication
Rapid and Ultrasensitive Sensor for Point-of-Use Detection of Perfluorooctanoic Acid Based on Molecular Imprinted Polymer and AC Electrothermal Effect
by Niloufar Amin, Jiangang Chen, Ngoc Susie Nguyen, Qiang He, John Schwartz and Jie Jayne Wu
Micromachines 2025, 16(3), 283; https://doi.org/10.3390/mi16030283 - 28 Feb 2025
Viewed by 236
Abstract
Perfluorooctanoic acid (PFOA) is one of the most persistent and bioaccumulative water contaminants. Sensitive, rapid, and in-field analysis is needed to ensure safe water supplies. Here, we present a single step (one shot) and rapid sensor capable of measuring PFOA at the sub-quadrillion [...] Read more.
Perfluorooctanoic acid (PFOA) is one of the most persistent and bioaccumulative water contaminants. Sensitive, rapid, and in-field analysis is needed to ensure safe water supplies. Here, we present a single step (one shot) and rapid sensor capable of measuring PFOA at the sub-quadrillion (ppq) level, 4.5 × 10−4 ppq, within 10 s. This innovative sensor employs a synergistic combination of a molecularly imprinted polymer (MIP)-modified gold interdigitated microelectrode chip and AC electrothermal effects (ACETs), which enhance detection sensitivity by facilitating the accelerated movement of PFOA molecules towards specific recognition sites on the sensing surface. The application of a predetermined AC signal induces microfluidic enrichment and results in concentration-dependent changes in interfacial capacitance during the binding process. This enables real-time, rapid quantification with exceptional sensitivity. We achieved a linear dynamic range spanning from 0.4 to 40 fg/L (4 × 10−7–4 × 10−5 ppt) and demonstrated good selectivity (~1:100) against other PFAS compounds, including perfluorooctanoic acid (PFOS), in PBS buffer. The sensor’s straightforward operation, cost-effectiveness, elimination of the need for external redox probes, compact design, and functionality in relatively resistant environmental matrices position it as an outstanding candidate for deployment in practical applications. Full article
(This article belongs to the Special Issue Innovations in Biosensors, Gas Sensors and Supercapacitors)
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<p>(<b>a</b>) A schematic of the PFOA-MIP film electropolymerization on the Au-IDME and a sensing mechanism based on the change in the electrical double layer (EDL). The top transparent layer shows the dielectric layer on the electrode’s surface. (<b>b</b>) A photo of a Au-IDME (<b>A</b>); the measurement setup for the ACEK-capacitive sensing (<b>B</b>); a simplified circuit model employed to represent the electrode–electrolyte interface (<b>C</b>). d<sub>EDL</sub>, d<sub>MIP</sub>, and d<sub>PFOA</sub>: the thickness of the EDL, MIP, and PFOA molecules.</p>
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<p>(<b>a</b>) Cyclic voltammograms during the electrodeposition of MIP layer on Au-IDME in acetate buffer (pH 5.8) with 10 mM o-PD and 1 mM PFOA (pink line), or 10 mM o-PD only (gray one); scan rate 50 mV/s; number of scans 25. Electrical spectrums for Au-IDME surface characterization. Impedance (<b>b</b>) and capacitance (<b>c</b>) spectrums before and after MIP electrodeposition and after template.</p>
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<p>(<b>a</b>) Normalized capacitances change as a function of time within 30 s for different levels of PFOA. (<b>b</b>) Capacitance change rates in response to different concentrations of PFOA, at 3 kHz and 100 mV.</p>
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<p>(<b>a</b>). MIP-AuIDME in the presence of the other PFAS compounds with similar head and alkyl groups. (<b>b</b>) The structure of the PFAS compounds studied.</p>
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18 pages, 1426 KiB  
Article
Association Between Per- and Polyfluoroalkyl Substances and All-Cause Mortality in Diabetic Patients: Insights from a National Cohort Study and Toxicogenomic Analysis
by Zhengxiao Wei, Jinyu Chen, Xue Mei and Yi Yu
Toxics 2025, 13(3), 168; https://doi.org/10.3390/toxics13030168 - 27 Feb 2025
Viewed by 178
Abstract
Per- and polyfluoroalkyl substances (PFAS) are a group of environmental contaminants associated with various health risks; however, their relationship with all-cause mortality in individuals with diabetes remains unclear. A total of 1256 participants from the National Health and Nutrition Examination Survey (NHANES) were [...] Read more.
Per- and polyfluoroalkyl substances (PFAS) are a group of environmental contaminants associated with various health risks; however, their relationship with all-cause mortality in individuals with diabetes remains unclear. A total of 1256 participants from the National Health and Nutrition Examination Survey (NHANES) were included to explore the association between seven PFAS compounds and all-cause mortality in diabetic patients. Preliminary logistic regression identified three PFAS compounds (perfluorooctanoic acid [PFOA], perfluorooctane sulfonic acid [PFOS], and 2-(N-methyl-PFOSA) acetate acid [MPAH]) as significantly associated with mortality in the diabetic population. The optimal cut-off values for PFOS, PFOA, and MPAH were determined using the X-tile algorithm, and participants were categorized into high- and low-exposure groups. Kaplan–Meier survival curves and multivariable Cox proportional hazards regression models were used to assess the relationship between PFAS levels and mortality risk. The results showed that high levels of PFOS were significantly associated with increased all-cause mortality risk in diabetic patients (hazard ratio [HR]: 1.55, 95% confidence interval [CI]: 1.06–2.29), while PFOA and MPAH showed no significant associations. To explore mechanisms underlying the PFOS–mortality link, toxicogenomic analysis identified 95 overlapping genes associated with PFOS exposure and diabetes-related mortality using the Comparative Toxicogenomics Database (CTD) and GeneCards. Functional enrichment analysis revealed key biological processes, such as glucose homeostasis and response to peptide hormone, with pathways including the longevity regulating pathway, apoptosis, and p53 signaling pathway. Protein–protein interaction network analysis identified 10 hub genes, and PFOS was found to upregulate or downregulate their mRNA expression, protein activity, or protein expression, with notable effects on mRNA levels. These findings suggest that PFOS exposure contributes to increased mortality risk in diabetic patients through pathways related to glucose metabolism, apoptosis, and cellular signaling. Our study provides new insights into the association between PFAS and all-cause mortality in diabetes, highlighting the need for large-scale cohort studies and further in vivo and in vitro experiments to validate these findings. Full article
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<p>Flowchart of the study population.</p>
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<p>Kaplan–Meier survival curves for all-cause mortality in the diabetic population, stratified by high and low levels of PFOS (<b>A</b>), PFOA (<b>B</b>), and MPAH (<b>C</b>). The sample sizes for each group were as follows: PFOS high exposure (<span class="html-italic">n</span> = 195), PFOS low exposure (<span class="html-italic">n</span> = 1061), PFOA high exposure (<span class="html-italic">n</span> = 1078), PFOA low exposure (<span class="html-italic">n</span> = 178), MPAH high exposure (<span class="html-italic">n</span> = 568), MPAH low exposure (<span class="html-italic">n</span> = 688). Abbreviations: PFOS, perfluorooctanesulfonic acid; PFOA, perfluorooctanoic acid; MPAH, 2-(N-methyl-PFOSA) acetate acid.</p>
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<p>Venn diagram of PFOS-related diabetes genes (CTD) and “Death due to diabetes” genes (GeneCards). Abbreviations: PFOS, perfluorooctanesulfonic acid; CTD, Comparative Toxicogenomics Database.</p>
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<p>Results of the toxicogenomic analysis: (<b>A</b>) The top five biological processes (BP), cellular component (CC), and molecular functions (MF) associated with diabetes-related mortality after PFOS exposure. (<b>B</b>) KEGG pathway analysis. (<b>C</b>) Identification of 10 hub genes from the PPI subnetwork. Abbreviations: PFOS, perfluorooctanesulfonic acid; BP, biological processes; CC, cellular component; MF, molecular functions; KEGG, Kyoto Encyclopedia of Genes and Genomes; PPI, protein-protein interaction; AKT1, AKT serine/threonine kinase 1; BCL2, B-cell leukemia/lymphoma 2 apoptosis regulator; CASP3, caspase 3; IL6, interleukin 6; TNF, tumor necrosis factor; PPARG, peroxisome proliferator-activated receptor gamma; SIRT1, sirtuin 1; INS, insulin; NFKB1, nuclear factor kappa B subunit 1; EGFR, epidermal growth factor receptor.</p>
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17 pages, 2496 KiB  
Review
Prevalent Per- and Polyfluoroalkyl Substances (PFASs) Pollution in Freshwater Basins in China: A Short Review
by Jingjing Zhang, Jiaoqin Liu, Riya Jin, Yina Qiao, Jipeng Mao and Zunyao Wang
Toxics 2025, 13(2), 135; https://doi.org/10.3390/toxics13020135 - 13 Feb 2025
Viewed by 595
Abstract
Organic pollutants like per- and polyfluoroalkyl substances (PFASs) exhibit persistence, bioaccumulation, resistance to degradation, and high toxicity, garnering significant attention from scholars worldwide. To better address and mitigate the environmental risks posed by PFASs, this paper employs bibliometric analysis to examine the literature [...] Read more.
Organic pollutants like per- and polyfluoroalkyl substances (PFASs) exhibit persistence, bioaccumulation, resistance to degradation, and high toxicity, garnering significant attention from scholars worldwide. To better address and mitigate the environmental risks posed by PFASs, this paper employs bibliometric analysis to examine the literature on PFASs’ concentrations collected in the Web of Science (WoS) database between 2019 and 2024. The results show that the overall trend of PFASs’ pollution research is relatively stable and increasing. In addition, this study also summarizes the pollution status of traditional PFASs across different environmental media in typical freshwater basins. It analyzes PFASs’ concentrations in surface water, sediment, and aquatic organisms, elucidating their distribution characteristics and potential sources. While perfluorooctanoic acid (PFOA) and perfluorooctane sulfonic acid (PFOS) levels in water environments are declining annually, short-chain PFASs and their substitutes are emerging as primary pollutants. Short-chain PFASs are frequently detected in surface water, whereas long-chain PFASs tend to accumulate in sediments. In aquatic organisms, PFASs are more likely to concentrate in protein-rich organs and tissues. The environmental presence of PFASs is largely influenced by human activities, such as metal plating, fluoride industry development, and industrial wastewater discharge. Currently, the development of PFASs in China faces a complex dilemma, entangled by policy and legal constraints, industrial production demands, the production and use of new alternatives, and their regulation and restriction, creating a vicious cycle. Breaking this deadlock necessitates continuous and active scientific research on PFASs, particularly PFOS, with an emphasis on detailed investigations of environmental sources and sinks. Furthermore, ecological and health risk assessments were conducted using Risk Quotient (RQ) and Hazard Quotient (HQ) methods. Comprehensive comparison indicates that PFASs (such as PFOA) in the majority of freshwater basins are at a low-risk level (RQ < 0.1 or HQ < 0.2), PFOS in some freshwater basins is at a medium-risk level (0.1 < RQ < 1), and no freshwater basin is at a high-risk level. The adsorption and removal approaches of PFASs were also analyzed, revealing that the combination of multiple treatment technologies as a novel integrated treatment technology holds excellent prospects for the removal of PFASs. Full article
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<p>WOS Annual publication on PFASs.</p>
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<p>WOS keyword network map.</p>
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<p>PFAS concentrations in surface water of typical basins in China (ng/L).</p>
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<p>PFASs’ mass fraction in the surface water of typical watershed in China (%).</p>
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<p>PFAS concentrations in the sediment of typical basins in China (ng/g).</p>
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<p>PFASs’ mass fraction in the sediments of typical watersheds in China (%).</p>
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<p>(<b>a</b>) Mass fraction of short/long chains in sediment (%) and (<b>b</b>) mass fraction of short/long chains in surface water (%).</p>
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24 pages, 7291 KiB  
Article
Piranha Foraging Optimization Algorithm with Deep Learning Enabled Fault Detection in Blockchain-Assisted Sustainable IoT Environment
by Haitham Assiri
Sustainability 2025, 17(4), 1362; https://doi.org/10.3390/su17041362 - 7 Feb 2025
Viewed by 603
Abstract
As the acceptance of Internet of Things (IoT) systems quickens, guaranteeing their sustainability and reliability poses an important challenge. Faults in IoT systems can result in resource inefficiency, high energy consumption, reduced security, and operational downtime, obstructing sustainability goals. Thus, blockchain (BC) technology, [...] Read more.
As the acceptance of Internet of Things (IoT) systems quickens, guaranteeing their sustainability and reliability poses an important challenge. Faults in IoT systems can result in resource inefficiency, high energy consumption, reduced security, and operational downtime, obstructing sustainability goals. Thus, blockchain (BC) technology, known for its decentralized and distributed characteristics, can offer significant solutions in IoT networks. BC technology provides several benefits, such as traceability, immutability, confidentiality, tamper proofing, data integrity, and privacy, without utilizing a third party. Recently, several consensus algorithms, including ripple, proof of stake (PoS), proof of work (PoW), and practical Byzantine fault tolerance (PBFT), have been developed to enhance BC efficiency. Combining fault detection algorithms and BC technology can result in a more reliable and secure IoT environment. Thus, this study presents a sustainable BC-Driven Edge Verification with a Consensus Approach-enabled Optimal Deep Learning (BCEVCA-ODL) approach for fault recognition in sustainable IoT environments. The proposed BCEVCA-ODL technique incorporates the merits of the BC, IoT, and DL techniques to enhance IoT networks’ security, trustworthiness, and efficacy. IoT devices have a substantial level of decentralized decision-making capacity in BC technology to achieve a consensus on the accomplishment of intrablock transactions. A stacked sparse autoencoder (SSAE) model is employed to detect faults in IoT networks. Lastly, the Piranha Foraging Optimization Algorithm (PFOA) approach is used for optimum hyperparameter tuning of the SSAE approach, which assists in enhancing the fault recognition rate. A wide range of simulations was accomplished to highlight the efficacy of the BCEVCA-ODL technique. The BCEVCA-ODL technique achieved a superior FDA value of 100% at a fault probability of 0.00, outperforming the other evaluated methods. The proposed work highlights the significance of embedding sustainability into IoT systems, underlining how advanced fault detection can provide environmental and operational benefits. The experimental outcomes pave the way for greener IoT technologies that support global sustainability initiatives. Full article
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<p>The overall workflow of the BCEVCA-ODL approach for IoT fault detection combines BC, IoTs, and DL techniques to enhance IoT networks’ security, trustworthiness, and effectiveness. The figure depicts the key stages of the model, including data collection, fault detection, and the implementation of security measures for reliable IoT performance.</p>
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<p>The architecture of BC technology illustrates the components involved in the decentralized system. The figure depicts multiple BC nodes responsible for processing and validating transactions. Each node maintains a timestamp and records transactions later grouped into blocks. Block 0 represents the initial block in the BC, with subsequent blocks linked to it to form a secure, immutable chain of transactions. This architecture ensures transparency, security, and reliability within the BC network.</p>
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<p>The SSAE approach’s structure illustrates the model’s key components. The process begins with input images sent to the system for analysis and processed by an RF model for feature extraction. The processed data are then passed through the autoencoder’s input layer, followed by multiple hidden layers that perform additional feature learning and transformation. Finally, the output layer gives the classification output, depicting the model’s final decision based on the learned features. This structure enables effective feature learning and classification.</p>
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<p>The steps involved in the PFOA model. The process begins with the initialization phase, where the parameters of the PFOA are set, and the positions of the piranhas are arbitrarily initialized. Next, the fitness of each piranha is computed to analyze their performance. The best-performing piranha is then identified. The model proceeds with the foraging behavior, comprising local and global search strategies to explore potential solutions. Boundary conditions are checked to ensure the positions remain valid. Afterwards, the fitness of the new positions of the piranhas is computed. The process continues iteratively, aiming to find the optimum optimal outcome. The algorithm stops once the optimal solution is detected and returned.</p>
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<p>(PR, %) outcomes of the BCEVCA-ODL technique across diverse classes. The table compares the PR results for the BCEVCA-ODL technique against other methods in detecting faults across various classes. The values presented represent the performance in terms of precision, illustrating how the BCEVCA-ODL technique performs relative to alternative approaches for each class.</p>
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<p>(RR, %) outcomes of the BCEVCA-ODL technique across various classes. The figure compares the RR results of the BCEVCA-ODL technique with other methods, across five classes: CPUHog, MemoryOF, Scanning, IOHog, and DOS. The data illustrate the recall performance of each method, illustrating how the BCEVCA-ODL method performs relative to alternative approaches in detecting faults across these diverse categories.</p>
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<p>(AR, %) outcomes of the BCEVCA-ODL technique across diverse classes. The figure compares the AR results of the BCEVCA-ODL technique with other methods computed on five distinct classes: CPUHog, MemoryOF, Scanning, IOHog, and DOS. The data illustrate each method’s accuracy, accentuating the superior performance of the BCEVCA-ODL technique in achieving high accuracy for fault detection across these classes.</p>
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<p>(FR, %) outcomes of the BCEVCA-ODL technique across diverse classes. The figure compares the FR results of the BCEVCA-ODL technique with other methods computed on five classes: CPUHog, MemoryOF, Scanning, IOHog, and DOS. The data demonstrate the performance of each method in terms of the F-score, highlighting the superior capability of the BCEVCA-ODL model to achieve higher F-scores across all the classes, indicating its efficiency in fault detection.</p>
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<p>(FDA, %) outcomes of the BCEVCA-ODL technique across various fault probabilities. The figure illustrates the FDA performance of the BCEVCA-ODL technique in comparison to other methods under diverse fault probability levels ranging from 0.05 to 0.50. The data show how each method performs as fault probability increases, highlighting the capability of the BCEVCA-ODL model to maintain superior accuracy in fault detection across all probability levels.</p>
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<p>(FAR, %) outcomes of the BCEVCA-ODL technique across diverse fault probabilities. The figure compares the FAR results of the BCEVCA-ODL technique with other methods under fault probability levels ranging from 0.1 to 0.5. The data show how the FAR varies with increasing fault probabilities, emphasizing the performance of each technique in minimizing false alarms. The figure illustrates the effectiveness of the BCEVCA-ODL model in maintaining a lower FAR compared to other approaches across all fault probability levels.</p>
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<p><math display="inline"><semantics> <mrow> <mi>A</mi> <mi>c</mi> <mi>c</mi> <msub> <mrow> <mi>u</mi> </mrow> <mrow> <mi>y</mi> </mrow> </msub> </mrow> </semantics></math> curve of the BCEVCA-ODL technique showing TRA and TES accuracy over epochs. The figure illustrates the change in accuracy for both TRA and TES datasets as the model progresses through epochs. The curve reflects the performance of the BCEVCA-ODL technique, with the accuracy increasing and stabilizing across the epochs. The plot provides insights into the model’s capability to learn and generalize over time, highlighting how the TRA and TES accuracy increase during the learning process.</p>
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<p>The loss curve of the BCEVCA-ODL technique shows TRA and TES loss over epochs. The figure illustrates the change in loss values for both TRA and TES datasets as the model progresses through epochs. It shows how the TRA and TES losses decrease over time, reflecting the model’s learning process and its capability to mitigate errors during TRA. This curve provides insights into the convergence behavior of the BCEVCA-ODL model, demonstrating how well the model fits the data and generalizes over the epochs.</p>
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<p>CT analysis of the BCEVCA-ODL technique compared to existing models. The figure presents the computational time (in seconds) for the BCEVCA-ODL technique and other methods comprising CSADL-DEVM, BDEV-CAML, PSO-DAWRF, NFD, and ETXTD. The data show the computational efficiency of each method, with the BCEVCA-ODL technique accentuating the shortest processing time compared to the others. This analysis underscores the superior speed and effectualness of the BCEVCA-ODL model in performing the task across the evaluated methods.</p>
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13 pages, 1362 KiB  
Article
The Distribution and Seasonality of Per- and Polyfluoroalkyl Substances (PFAS) in the Vertical Water Column of a Stratified Eutrophic Freshwater Lake
by Patrick R. Gorski
Environments 2025, 12(2), 48; https://doi.org/10.3390/environments12020048 - 4 Feb 2025
Viewed by 524
Abstract
The vertical distribution and potential variability of Per- and Polyfluoroalkyl substances (PFAS) in the water column of lacustrine systems is important to know for sampling and monitoring purposes, but could also relate to details of their fate, transport, and distribution. In this study, [...] Read more.
The vertical distribution and potential variability of Per- and Polyfluoroalkyl substances (PFAS) in the water column of lacustrine systems is important to know for sampling and monitoring purposes, but could also relate to details of their fate, transport, and distribution. In this study, the water column of a eutrophic freshwater lake (Lake Monona, Madison, WI, USA) was sampled vertically for PFAS during summer stratification at several depths (surface microlayer to 1 m from the bottom) and then monitored at four dates and three depths the following year to assess seasonality. PFAS concentration did not exhibit vertical stratification or large variability in the water column. However, seasonal variation in PFAS concentration was detected, as well as an increase in PFAS concentration related to drought conditions. This study suggests that a surface water grab sample may be a sufficient representative of the water column for the basic monitoring of PFAS. But a single sample during the year may not provide a complete understanding of the lake, and multiple samples should be taken to capture and understand important seasonal events. Full article
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<p>Water column profiles of parameters in Lake Monona on August 4, 2022. (<b>a</b>) Chemical and biological parameters (Temp, DO, pH, Chl <span class="html-italic">a</span>, TSS, Org TSS, and alkalinity) plotted against water column depth. Parameters show the lake was highly stratified, with Chl <span class="html-italic">a</span>, TSS, and Org TSS at much higher levels in the epilimnion than the hypolimnion. (<b>b</b>) PFOS and PFOA concentrations plotted against water column depth. Symbols represent individual measurements, except 6 m, which is an average. PFOS and PFOA concentrations were consistent with depth during stratification unlike most other parameters.</p>
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<p>(<b>a</b>) Concentrations of 13 PFAS at each sample depth, in the surface microlayer (SML) and in a surface water grab sample (grab). Concentrations at each depth are for individual samples except at 6 m, which is the average of two samples. Only detectable PFAS are shown. The concentration of most compounds was similar across all depths. (<b>b</b>) The same data as in (<b>a</b>) but expressed in relative percentage of PFAS at each depth.</p>
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<p>Lake Monona water column profiles of temperature on four sampling dates in 2023: May 11, July 11, September 20, and December 21.</p>
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<p>(<b>a</b>) Concentrations of 13 PFAS on four dates and at three depths (grab, mid-epilimnion, and mid-hypolimnion) in Lake Monona. Only detectable PFAS are shown. Total PFAS concentration changes during the year, with a peak during September. (<b>b</b>) Relative percentages of individual PFAS were similar across depths on each date and consistent throughout the year despite monthly differences in concentration.</p>
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<p>Lake Monona (MON) grab sample PFAS concentrations compared with Starkweather Creek (STKW) grab samples on the same dates in 2023. NS = no sample.</p>
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<p>The 2023 rainfall in Madison, WI [<a href="#B24-environments-12-00048" class="html-bibr">24</a>] highlighting the 4 sampling dates with red arrows on the <span class="html-italic">x</span>-axis.</p>
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31 pages, 5667 KiB  
Article
Protective Effects of Resveratrol Against Perfluorooctanoic Acid-Induced Testicular and Epididymal Toxicity in Adult Rats Exposed During Their Prepubertal Period
by R. Pavani, K. Venkaiah, P. Gnana Prakasam, Vijaya R. Dirisala, P. Gopi Krishna, B. Kishori and S. B. Sainath
Toxics 2025, 13(2), 111; https://doi.org/10.3390/toxics13020111 - 29 Jan 2025
Viewed by 574
Abstract
The antioxidant properties of resveratrol (RES) against oxidative toxicity induced by testicular toxicants are well documented. The current study aimed to investigate the probable beneficial role of RES on male reproduction in adult rats following prepubertal exposure to perfluorooctanoic acid (PFOA). Healthy rats [...] Read more.
The antioxidant properties of resveratrol (RES) against oxidative toxicity induced by testicular toxicants are well documented. The current study aimed to investigate the probable beneficial role of RES on male reproduction in adult rats following prepubertal exposure to perfluorooctanoic acid (PFOA). Healthy rats of the Wistar strain (23 days old) were allocated into four groups. Rats in group I did not receive any treatment, while rats in groups II, III, and IV received RES, PFOA, and RES + PFOA, respectively, between days 23 and 56 and were monitored for up to 90 days. Exposure to PFOA resulted in a significant reduction in spermiogram parameters, testicular 3β- and 17β-HSD activity levels, and circulatory levels of testosterone. A significant elevation in LPx, PCs, H2O2, and O2, associated with a concomitant reduction in SOD, CAT, GPx, GR, and GSH, was noticed in the testes, as well as region-specific changes in pro- and antioxidants in the epididymides of exposed rats compared to controls. A significant increase in serum FSH and LH, testicular cholesterol levels, and caspase-3 activity was observed in PFOA-exposed rats compared to controls. Histological analysis revealed that the integrity of the testes was deteriorated in PFOA-exposed rats. Transcriptomic profiling of the testes and epididymides revealed 98 and 611 altered genes, respectively. In the testes, apoptosis and glutathione pathways were disrupted, while in the epididymides, glutathione and bile secretion pathways were altered in PFOA-exposed rats. PFOA exposure resulted in the down-regulation in the testes of 17β-HSD, StAR, nfe2l2, ar, Lhcgr, and mRNA levels, associated with the up-regulation of casp3 mRNA, and down-regulation of alpha 1 adrenoceptor, muscarinic choline receptor 3, and androgen receptor in the epididymides of exposed rats compared to the controls. These events might lead to male infertility in PFOA-exposed rats. In contrast, restoration of selected reproductive variables was observed in RES plus PFOA-exposed rats compared to rats exposed to PFOA alone. Taken together, we postulate that prepubertal exposure to PFOA triggered oxidative damage and altered genes in the testes and epididymides, leading to suppressed male reproductive health in adult rats, while RES, with its steroidogenic, antiapoptotic, and antioxidant effects, restored PFOA-induced fertility potential in rats. Full article
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<p>Photomicrograph of sperm from control and Perfluorooctanoic acid (PFOA) exposed rats. (<b>A</b>): Normal sperm with a characteristic hook shaped head (control rats). (<b>B</b>,<b>C</b>): Sperm head abnormalities, such as pin shaped head, no head, or a broken head (PFOA exposed rats). Note: the characteristic hook shape was missing in the sperm of PFOA exposed rats.</p>
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<p>Sperm DNA damage in rats in the control, perfluorooctanoic acid (PFOA) exposed, resveratrol (RES) treated, and PFOA exposed plus RES supplementation groups, as revealed by comet assay. Bars in white did not differ significantly from each other. Asterisks indicate a significant difference at <span class="html-italic">p</span> &lt; 0.001 over control. ns = non-significant.</p>
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<p>Photomicrographs of the testicular architecture of control (<b>A</b>), resveratrol (RES, (<b>B</b>)), perfluorooctanoic acid (PFOA, (<b>C</b>)), and PFOA plus RES (<b>D</b>) treated rats. Scale bar: 50 µm.</p>
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<p>Deregulated genes in the testis of perfluorooctanoic acid exposed rats, shown in the form of Circos plot genes.</p>
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<p>A KEGG pathway analysis of up-regulated genes indicated disruption (top most pathway: fold enrichment value of 98.66) of the apoptosis pathway in the testis of rats exposed to perfluorooctanoic acid during the prepubertal period; * represents genes disrupted in the apoptotic pathway; FADD: Fas associated death domain; APAF1: apoptotic peptidase activating factor 1; BAX: B cell lymphoma 2 associated X; casp7: caspase 7; casp3: caspase 3.</p>
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<p>A KEGG pathway analysis of down-regulated genes (Fold enrichment value: 26.8) indicated disruption of the glutathione pathway in the testis and up-regulated genes (Fold enrichment value: 6.4) in the epididymis of rats exposed to perfluorooctanoic acid during the prepubertal period. * represent genes disrupted in glutathione pathway in testis. Glutathione peroxidase 3 (gpx3), glutathione peroxidase 4 (gpx4), glutathione synthetase (gss) and glutathione reductase (gsr). Red colour box represent genes disrupted in glutathione pathway in epididymis. glutathione specific glutamylcyclotransferase 1 (chac1), glutathione S transferase alpha 2 (gst2) and glutathione S transferase alpha 5 (gst5).</p>
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<p>A KEGG pathway analysis of down-regulated genes (Fold enrichment value: 4.4) revealed disruption of the bile secretion pathway in the epididymis of rats exposed to perfluorooctanoic acid during the prepubertal period. * represent genes disrupted in bile secretion pathway. ATP binding cassette subfamily C member 4 (Abcc4), CF transmembrane conductance regulator (Cftr), adenylate cyclase 1(Adcy1), adenylate cyclase 1(Adcy2), aquaporin 1(Aqp1), aquaporin 9 (Aqp 9), secretin receptor (Sctr), solute carrier family 4 member 4(Slc4a4), solute carrier family 5 member 1 (Slc5a1).</p>
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17 pages, 948 KiB  
Article
Concentrations of Serum Per- and Polyfluoroalkyl Substances and Lipid Health in Adolescents: A Cross-Sectional Study from the Korean National Environmental Health Survey 2018–2020
by Min-Won Shin, Habyeong Kang and Shin-Hye Kim
Toxics 2025, 13(2), 91; https://doi.org/10.3390/toxics13020091 - 25 Jan 2025
Viewed by 598
Abstract
Emerging evidence indicates that environmental exposure to per- and polyfluoroalkyl substances (PFASs) may influence lipid metabolism, though studies on adolescents remain scarce. This study aimed to investigate the association between PFAS mixture exposure and lipid profiles in Korean adolescents. Using data from the [...] Read more.
Emerging evidence indicates that environmental exposure to per- and polyfluoroalkyl substances (PFASs) may influence lipid metabolism, though studies on adolescents remain scarce. This study aimed to investigate the association between PFAS mixture exposure and lipid profiles in Korean adolescents. Using data from the Korean National Environmental Health Survey (2018–2020), we analyzed 824 adolescents aged 12–17 years. Serum concentrations of PFAS, including perfluorooctanoic acid (PFOA), perfluorooctane sulfonic acid (PFOS), perfluorononanoic acid (PFNA), perfluorohexane sulfonic acid (PFHxS), and perfluorodecanoic acid (PFDeA), and lipid profiles were assessed. In multivariate regression models, PFDeA and PFNA were positively associated with elevated total cholesterol and low-density lipoprotein cholesterol levels, and PFDeA was associated with hypercholesterolemia risk in boys. In girls, PFDeA was associated with higher high-density lipoprotein cholesterol and lower triglycerides, though no significant association with hypercholesterolemia risk was observed. Bayesian kernel machine regression demonstrated positive associations between PFAS mixture exposure and hypercholesterolemia risk in boys but not in girls. The quantile g-computation model also demonstrated an odds ratio (OR) of 1.47 (95% CI: 0.99–2.19, p = 0.057) for PFAS mixture exposure in boys, suggesting borderline statistical significance. These findings suggest that PFAS exposure may disrupt lipid metabolism, elevating hypercholesterolemia risk in adolescents, particularly boys. Full article
(This article belongs to the Special Issue Health Effects and Toxicology Studies of Emerging Contaminants)
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<p>Adjusted percentage differences in lipid profiles of the highest tertiles of per- and polyfluoroalkyl substance (PFAS) concentrations compared with the lowest tertiles. Adjusted for age, central obesity, family income, smoking status, alcohol habits, walking habits, consumption of mass-produced food items/seafood, and menstruation years (girls). Squares represent adjusted percentage differences, and horizontal lines represent 95% confidence intervals. Red squares represent statistically significant differences (<span class="html-italic">p</span> &lt; 0.05). CI, confidence interval; PFOA, perfluorooctanoic acid; PFOS, perfluorooctane sulfonic acid; PFNA, perfluorononanoic acid; PFHxS, perfluorohexane sulfonic acid; PFDeA, perfluorodecanoic acid; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; non-HDL-C, non-high-density lipoprotein cholesterol; TG, triglycerides.</p>
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<p>Odds ratios (95% CI) of subnormal lipid profiles of the highest tertiles of per- and polyfluoroalkyl substance (PFAS) concentrations compared with the lowest tertiles. Adjusted for age, central obesity, family income, smoking status, alcohol habits, walking habits, consumption of mass-produced food items/seafood, and menstruation years (girls). Squares represent adjusted odds ratios, and horizontal lines represent 95% confidence intervals. Red squares represent statistically significant differences (<span class="html-italic">p</span> &lt; 0.05). CI, confidence interval; PFOA, perfluorooctanoic acid; PFOS, perfluorooctane sulfonic acid; PFNA, perfluorononanoic acid; PFHxS, perfluorohexane sulfonic acid; PFDeA, perfluorodecanoic acid; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; non-HDL-C, non-high-density lipoprotein cholesterol.</p>
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<p>The combined effects of exposure to per- and polyfluoroalkyl substance (PFAS) mixtures on hypercholesterolemia were analyzed using the Bayesian kernel machine regression (BKMR) model. Each quantile level was compared with the 0.5th quantile as the baseline. The chart shows the estimated effects, along with the 95% confidence intervals. Adjusted for age, central obesity, family income, smoking status, alcohol habits, walking habits, consumption of mass-produced food items/seafood, and menstruation years (girls).</p>
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32 pages, 3057 KiB  
Review
Review of Emerging and Nonconventional Analytical Techniques for Per- and Polyfluoroalkyl Substances (PFAS): Application for Risk Assessment
by Andrew McQueen, Ashley Kimble, Paige Krupa, Anna Longwell, Alyssa Calomeni-Eck and David Moore
Water 2025, 17(3), 303; https://doi.org/10.3390/w17030303 - 22 Jan 2025
Cited by 1 | Viewed by 716
Abstract
Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants that pose significant risks to ecosystems and human health. Increasing regulatory demands for PFAS management have increased the need for rapid and deployable analytical technologies for both abiotic and biotic matrices. Traditional detection methods, [...] Read more.
Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants that pose significant risks to ecosystems and human health. Increasing regulatory demands for PFAS management have increased the need for rapid and deployable analytical technologies for both abiotic and biotic matrices. Traditional detection methods, such as standardized chromatography, often require weeks to months for analysis due to a limited number of appropriately accredited laboratories, delaying critical decision-making. This literature review is intended to identify promising emerging PFAS analytical techniques or technologies to facilitate more rapid (near real-time) analysis and explore their relevancy in supporting human and ecological risk assessments. Recently developed optical and electrochemical sensing approaches are enabling the detection of PFASs within minutes to hours, with detection limits typically aligning within reported ambient concentrations in water, soil, and sediment. These emerging technologies could (1) support planning and prioritization of sampling efforts during the problem formulation phase of risk assessment, (2) complement traditional chromatography methods to lower time and resource demands to improve sampling frequency over space and time, and (3) aid in risk-informed characterization of PFAS exposures based on identified chemical classes or groups. This review highlights those approaches and technologies that could potentially enhance the comprehensiveness and efficiency of PFAS risk assessment across diverse environmental settings in the future. Full article
(This article belongs to the Section Water Quality and Contamination)
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<p>Reported PFOA detection limits as compared to ambient background concentrations and conventional analytical detection limits. Note: LC-MS/MS method detection limit (MDL) for PFOA based on USEPA method 537.1 (PFOA = 0.00053 ppb (µg/L)) [<a href="#B82-water-17-00303" class="html-bibr">82</a>]; USEPA legally enforceable maximum contaminant levels (MCLs) for PFOA = 0.004 ppb (µg/L), <sup>1</sup> Reported ambient concentration ranges for PFOA for surface and groundwater based on the work of Javis et al. [<a href="#B71-water-17-00303" class="html-bibr">71</a>] and Johnson et al. [<a href="#B76-water-17-00303" class="html-bibr">76</a>]; <sup>2</sup> Reported ambient PFOA concentration ranges for soil and sediment based on the work of Rankin et al. [<a href="#B78-water-17-00303" class="html-bibr">78</a>] and Vedagiri et al. [<a href="#B79-water-17-00303" class="html-bibr">79</a>]. Optical references: [<a href="#B26-water-17-00303" class="html-bibr">26</a>,<a href="#B27-water-17-00303" class="html-bibr">27</a>,<a href="#B30-water-17-00303" class="html-bibr">30</a>,<a href="#B31-water-17-00303" class="html-bibr">31</a>,<a href="#B34-water-17-00303" class="html-bibr">34</a>,<a href="#B35-water-17-00303" class="html-bibr">35</a>,<a href="#B36-water-17-00303" class="html-bibr">36</a>,<a href="#B37-water-17-00303" class="html-bibr">37</a>,<a href="#B38-water-17-00303" class="html-bibr">38</a>,<a href="#B41-water-17-00303" class="html-bibr">41</a>]; electrochemical references: [<a href="#B43-water-17-00303" class="html-bibr">43</a>,<a href="#B45-water-17-00303" class="html-bibr">45</a>,<a href="#B46-water-17-00303" class="html-bibr">46</a>]; nonconventional references: [<a href="#B52-water-17-00303" class="html-bibr">52</a>,<a href="#B53-water-17-00303" class="html-bibr">53</a>].</p>
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<p>Reported PFOS detection limits as compared to ambient background concentrations and conventional analytical detection limits. Note: LC-MS/MS method detection limit (MDL) for PFOS based on USEPA method 537.1 for PFOS = 0.004 ppb (µg/L) [<a href="#B82-water-17-00303" class="html-bibr">82</a>]; USEPA legally enforceable maximum contaminant levels (MCLs) for PFOS = 0.004 ppb (µg/L), <sup>1</sup> Reported ambient concentration ranges for PFOA for surface and groundwater based on the work of Javis et al. [<a href="#B71-water-17-00303" class="html-bibr">71</a>] and Johnson et al. [<a href="#B76-water-17-00303" class="html-bibr">76</a>]; <sup>2</sup> Reported ambient PFOA concentration ranges for soil and sediment based on the work of Rankin et al. [<a href="#B78-water-17-00303" class="html-bibr">78</a>] and Vedagiri et al. [<a href="#B79-water-17-00303" class="html-bibr">79</a>]. Optical references: [<a href="#B25-water-17-00303" class="html-bibr">25</a>,<a href="#B27-water-17-00303" class="html-bibr">27</a>,<a href="#B28-water-17-00303" class="html-bibr">28</a>,<a href="#B30-water-17-00303" class="html-bibr">30</a>,<a href="#B32-water-17-00303" class="html-bibr">32</a>,<a href="#B33-water-17-00303" class="html-bibr">33</a>,<a href="#B34-water-17-00303" class="html-bibr">34</a>,<a href="#B35-water-17-00303" class="html-bibr">35</a>,<a href="#B36-water-17-00303" class="html-bibr">36</a>,<a href="#B37-water-17-00303" class="html-bibr">37</a>,<a href="#B41-water-17-00303" class="html-bibr">41</a>]; electrochemical references: [<a href="#B44-water-17-00303" class="html-bibr">44</a>,<a href="#B45-water-17-00303" class="html-bibr">45</a>,<a href="#B47-water-17-00303" class="html-bibr">47</a>,<a href="#B48-water-17-00303" class="html-bibr">48</a>,<a href="#B50-water-17-00303" class="html-bibr">50</a>]; nonconventional references: [<a href="#B69-water-17-00303" class="html-bibr">69</a>].</p>
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<p>Conceptual integration of emerging technologies within risk assessment processes. Examples of ecological risk analysis framework (<b>A</b>), tiered framework for screening chemical and advanced materials (<b>B</b>); and proposed hybrid relationship between emerging and standardized approaches for PFAS analysis for risk-based processes (<b>C</b>). Note: panel (<b>A</b>) based on the USEPA ecological risk framework [<a href="#B17-water-17-00303" class="html-bibr">17</a>,<a href="#B18-water-17-00303" class="html-bibr">18</a>]; panel (<b>B</b>) based on Moore et al.’s [<a href="#B86-water-17-00303" class="html-bibr">86</a>] tiered process flowchart to improve assessment, monitoring and adaptive management of emerging contaminants.</p>
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15 pages, 3699 KiB  
Article
Contrasting Perfluorooctanoic Acid Removal by Calcite Before and After Heat Treatment
by Zhaohui Li, Yating Yang, Yaqi Wen, Yuhan Li, Jeremy Moczulewski, Po-Hsiang Chang, Stacie E. Albert and Lori Allen
Environments 2025, 12(1), 29; https://doi.org/10.3390/environments12010029 - 17 Jan 2025
Viewed by 556
Abstract
Calcites before and after calcination at 1000 °C were evaluated for their potential removal of perfluorooctanoic acid (PFOA) from water. After heat treatment, the PFOA sorption capacity increased by 25%, from 3.2 to 3.9 mg g−1, and the affinity increased by [...] Read more.
Calcites before and after calcination at 1000 °C were evaluated for their potential removal of perfluorooctanoic acid (PFOA) from water. After heat treatment, the PFOA sorption capacity increased by 25%, from 3.2 to 3.9 mg g−1, and the affinity increased by 2.7 times, from 0.03 to 0.08 L mg−1. Kinetically, the initial rate, rate constant, and equilibrium sorption were 8.7 mg g−1 h−1, 2.6 g mg−1 h−1, and 1.8 mg g−1 for heat treated calcite, in comparison to 6.4 mg g−1 h−1, 3.1 g mg−1 h−1, and 1.4 mg g−1 for calcite without heat treatment. X-ray diffraction analyses showed phase changing from calcite to calcium oxide after calcination. However, after contact with PFOA solutions for 24 h, the major phase changed back to calcite with a minute amount of Ca(OH)2. These results suggest that using raw cement materials derived from heat treatment of limestone may be a good option for the removal of PFOA from water. Thus, further studies are needed to confirm this claim. Full article
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<p>Sorption of PFOA on Cal and HCal. The solid lines are the Langmuir model fitting of the observed data. The right <span class="html-italic">y</span>-axis with solid symbols is the percentage of PFOA sorbed.</p>
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<p>Sorption kinetics of PFOA on Cal and HCal without pH adjustment under an initial concentration of 100 mg L<sup>−1</sup>. The solid lines are pseudo-second-order model fitting of the observed data.</p>
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<p>PFOA removal by Cal as affected by equilibrium solution pH under an initial PFOA concentration of 100 mg L<sup>−1</sup>.</p>
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<p>PFOA removal by Cal and HCal as affected by equilibrium solution ionic strength under an initial PFOA concentration of 100 mg L<sup>−1</sup>.</p>
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<p>PFOA removal by Cal and HCal as affected by equilibrium solution temperature under an initial PFOA concentration of 100 mg L<sup>−1</sup>.</p>
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<p>FTIR spectra of Cal and HCal after in contact with different initial PFOA concentrations (H represents HCal), respectively. The numbers are the initial PFOA concentrations in mg L<sup>−1</sup>.</p>
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<p>XRD patterns of Cal (<b>a</b>) and HCal (<b>b</b>) after equilibrated with different initial concentrations of PFOA (numbers in mg L<sup>−1</sup>), and the standard samples of Ca(OH)<sub>2</sub> and CaO.</p>
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<p>TG (<b>a</b>) and DTG (<b>b</b>) analyses of Cal and HCal. The number is the initial PFOA concentration in mg L<sup>−1</sup>.</p>
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<p>SEM images of Cal (<b>a</b>) and HCal (<b>c</b>) and their SEM images after their sorption of PFOA from an initial concentration of 200 mg L<sup>−1</sup>, respectively (<b>b</b>,<b>d</b>). The EDS spectra of face and point scans of samples after in contact with PFOA solution for 24 h (<b>e</b>).</p>
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12 pages, 1884 KiB  
Article
Air Bubbling Assisted Soil Washing to Treat PFAS in High Organic Content Soils
by Kaushik Londhe and Arjun K. Venkatesan
Environments 2025, 12(1), 20; https://doi.org/10.3390/environments12010020 - 12 Jan 2025
Viewed by 774
Abstract
The soil-washing technique has been successfully utilized for the remediation of PFAS-contaminated soils. Prior studies have shown that the organic carbon (OC) content and grain size of soil determined the efficiency of PFAS removal during washing. However, most of the past studies have [...] Read more.
The soil-washing technique has been successfully utilized for the remediation of PFAS-contaminated soils. Prior studies have shown that the organic carbon (OC) content and grain size of soil determined the efficiency of PFAS removal during washing. However, most of the past studies have focused on soils with a low OC content, typically ranging from 0–3%. In this study, we explored the use of a novel process where soil washing was combined with air bubbling (or foam fractionation) to aid in the removal of PFAS from high OC-content soils (~4–20%). Treatment with air bubbling of high OC soil (~20%) with perfluorobutane sulfonate (PFBS) and perfluorooctanoate (PFOA) did not enhance their removal, as they featured low surface activity. However, we observed an improvement in the extraction of perfluorooctane sulfonate (PFOS) from 27% to 42% with bubbling, consistent with the higher surface activity of PFOS compared to PFOA and PFBS. Perfluorodecanoic acid (PFDA) was irreversibly adsorbed to the high OC soil and was not removed efficiently by both bubbling and soil washing. A slight improvement in PFDA removal (6–13%) was observed when a co-surfactant (cetyltrimethylammonium chloride) was added and when the OC content was reduced to ~4% by the addition of nonorganic sand to the contaminated soil prior to soil washing. This suggested that the interaction of PFDA with OC was the dominant factor determining its extraction from soil. In conclusion, our results indicated that soil washing alone was sufficient for the removal of short-chain PFAS from soil. Although bubbling had a mild effect on the removal of some long-chain PFAS from the solution, it did not help in the overall removal of PFAS from high OC soils, highlighting the difficulty in the treatment of high OC-content soils and that immobilization of PFAS would be an ideal approach in managing such contaminated sites. Full article
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<p>Schematic of the batch column setup for air-bubbling extraction of PFAS from soil.</p>
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<p>Removal percentages of PFOA, PFOS, and PFDA when present as single solute after 60 min of soil washing. Initial concentration 0.1 μg/g. Error bars represent variations in duplicate samples.</p>
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<p>Removal percentages of PFDA-contaminated soils for (<b>a</b>) varying pHs for the wash solution, (<b>b</b>) 1 mg/L CTAC addition (pH 7.3), and (<b>c</b>) at reduced OC content of soil (soil/sand ratio of 25:75), pH 7.3. Error bars represent variations in duplicate samples. Numbers above the bars represent the average percentage of PFAS accounted for after treatment.</p>
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<p>Extraction efficiency of (<b>a</b>) PFBS, (<b>b</b>) PFOA, (<b>c</b>) PFOS, and (<b>d</b>) PFDA from soil spiked with 0.5 nmol/g of each PFAS. Error bars represent variations in duplicate samples.</p>
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<p>Mass balance of PFAS in various fractions before and after treatment: (<b>a</b>) PFBS, (<b>b</b>) PFOA, (<b>c</b>) PFOS, and (<b>d</b>) PFDA. Blue bar represents the mass of PFAS contained in untreated soil. After treatment: The gray fraction is the mass of PFAS contained in the washing (aqueous) solution, green fraction is the mass of PFAS associated with unsettled solids (fines), and yellow fraction represents the mass of PFAS associated with the apparatus due to sorption.</p>
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17 pages, 3503 KiB  
Article
Comprehensive Assessment of Per- and Polyfluoroalkyl Substances (PFAS) Pollution in a Coastal Region: Contributions from Nearshore and Offshore Sources
by Chenyu Chen, Ying Wang, Fei Chen, Xinyue Wang, Qiao Zhang, Jialong Sun, Si Li, Qiang Chen, Fangze Shang and Hui Zhang
Water 2025, 17(2), 149; https://doi.org/10.3390/w17020149 - 8 Jan 2025
Viewed by 868
Abstract
Per- and polyfluoroalkyl substances (PFAS) have become a well-known class of anthropogenic pollutants in coastal regions. It is known that PFAS primarily enter the sea from nearshore sources, dry deposition, and wet deposition. However, the contribution of offshore sources to PFAS pollution in [...] Read more.
Per- and polyfluoroalkyl substances (PFAS) have become a well-known class of anthropogenic pollutants in coastal regions. It is known that PFAS primarily enter the sea from nearshore sources, dry deposition, and wet deposition. However, the contribution of offshore sources to PFAS pollution in the sea remains poorly understood. Our study aims to investigate the occurrence of 74 PFAS across 15 groups in a coastal region of eastern China and to characterize their spatial distribution by focusing on the critical roles of both nearshore and offshore sources. Results revealed that 26 PFAS were detected in the coastal region (i.e., Ou River and Wenzhou Bay), with detection frequencies ranging from 4.3% to 100.0%. Notably, over 10 PFAS were detected for the first time in the region, such as perfluorooctane sulfonamide (FOSA), hexafluoropropylene oxide dimer acid (HFPO-DA), and 6:2 fluorotelomer sulfonic acid (6:2 FTSA), among others. The concentrations of detected PFAS ranged from 0.0018 to 76.31 ng/L, with perfluorooctanoic acid (PFOA) as the dominant congener. Spatial analysis indicated that the nearshore area was more severely polluted compared to the offshore area, with specific hotspots identified near industrialized areas. However, the distribution of certain PFAS, such as perfluorobutane sulfonic acid (PFBS) and perfluoro-3,6-dioxaheptanoic acid (PFDHA), exhibited a contrasting pattern, with higher concentrations observed in the offshore area and near island perimeters. These findings suggest that PFAS pollution in Wenzhou Bay originates from both nearshore and offshore sources, highlighting a complex interplay between nearshore and island-related activities. Full article
(This article belongs to the Section Oceans and Coastal Zones)
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<p>Sampling sites in the Ou River (S1 and S2) and Wenzhou Bay (S3–S23), Zhejiang Province, China.</p>
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<p>PFAS concentrations in the surface water samples from the Ou River and Wenzhou Bay. Median (the middle line), minimum, and maximum values, excluding outliers (upper and lower whiskers), are shown in the boxplots. The black dots represent outliers. The blue dots represent PFAS concentrations at the sampling sites.</p>
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<p>Correlations between 17 PFAS detected with high detection frequency (&gt;50%). *: <span class="html-italic">p</span> &lt; 0.05, **: <span class="html-italic">p</span> &lt; 0.01, ***: <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Spatial distribution of PFAS at the 23 sampling sites. (<b>a</b>) Concentration profiles of individual PFAS; (<b>b</b>) concentration percentages of PFAS; (<b>c</b>) contributions of PFCA and other groups; and (<b>d</b>) contributions of long-chain and short-chain PFAS.</p>
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<p>Distribution of detected PFAS at the 23 sampling sites (data are normalized between −1.0 and 1.0).</p>
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<p>Concentrations of the 10 PFAS showing significant differences (<span class="html-italic">p</span> &lt; 0.05) between nearshore and offshore sites.</p>
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<p>PCA plot of the 23 sampling sites based on the detected PFAS.</p>
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20 pages, 2290 KiB  
Article
Impact of Short-Chain Perfluoropropylene Oxide Acids on Biochemical and Behavioural Parameters in Eisenia fetida (Savigny, 1826)
by Davide Rotondo, Davide Gualandris, Candida Lorusso, Albert Braeuning, Antonio Calisi and Francesco Dondero
J. Xenobiot. 2025, 15(1), 2; https://doi.org/10.3390/jox15010002 - 26 Dec 2024
Viewed by 794
Abstract
Per- and polyfluoroalkyl substances (PFAS) are a class of persistent organic pollutants that pose a growing threat to environmental and human health. Soil acts as a long-term reservoir for PFAS, potentially impacting soil biodiversity and ecosystem function. Earthworms, as keystone species in soil [...] Read more.
Per- and polyfluoroalkyl substances (PFAS) are a class of persistent organic pollutants that pose a growing threat to environmental and human health. Soil acts as a long-term reservoir for PFAS, potentially impacting soil biodiversity and ecosystem function. Earthworms, as keystone species in soil ecosystems, are particularly vulnerable to PFAS exposure. In this study, we investigated the sublethal effects of three short-chain (C4–C6) next-generation perfluoropropylene oxide acids (PFPOAs) on the earthworm Eisenia fetida, using a legacy perfluoroalkyl carboxylic acid (PFCA), perfluorooctanoic acid (PFOA), as a reference. We assessed a suite of biochemical endpoints, including markers for oxidative stress (catalase and superoxide dismutase activity), immunity (phenol oxidase activity), neurotoxicity (acetylcholinesterase activity), and behavioural endpoints (escape test). Results indicate that all tested PFAS, even at sub-micromolar concentrations, elicited significant effects across multiple physiological domains. Interestingly, HFPO-DA demonstrated the most substantial impact across all endpoints tested, indicating broad and significant biochemical and neurotoxic effects. Our findings underscore the potential risks of both legacy and emerging PFAS to soil ecosystems, emphasising the need for further research to understand the long-term consequences of PFAS contamination. Full article
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Graphical abstract
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<p>CAT (catalase) activity. Data are presented as the mean activity per mg of protein ± standard error of the mean (SEM). Statistical significance was determined by the Kruskal–Wallis test followed by a post hoc Dunn’s multiple comparison test. * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001; **** <span class="html-italic">p</span> &lt; 0.0001; ns, not statistically significant. The x-axis reports PFAS concentrations in μM, and Ctr represents vehicle-exposed earthworms. Lines above the histogram bars indicate statistical significance between PFAS-treated groups and the control. Each panel represents results for a different PFAS compound: (<b>a</b>) HFPO-DA, (<b>b</b>) PFMOBA, (<b>c</b>) PFMOPrA, and (<b>d</b>) PFOA.</p>
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<p>SOD (superoxide dismutase) activity. Statistical significance was determined by ANOVA followed by a post hoc Holm–Sidak’s multiple comparison test. * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; and *** <span class="html-italic">p</span> &lt; 0.001; ns, not statistically significant. The line above the histogram bars indicates statistical significance between PFAS-treated groups and the control. Each panel represents results for a different PFAS compound: (<b>a</b>) HFPO-DA, (<b>b</b>) PFMOBA, (<b>c</b>) PFMOPrA, and (<b>d</b>) PFOA. See caption to <a href="#jox-15-00002-f001" class="html-fig">Figure 1</a> for more details.</p>
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<p>Phenol oxidase. A semi-quantitative evaluation was performed following the absorption (abs) at 590 nm due to L-DOPA oxidation. Statistical significance was determined by the Kruskal–Wallis test followed by a post hoc Dunn’s multiple comparison test. ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001; and **** <span class="html-italic">p</span> &lt; 0.0001; ns, not statistically significant. The line above the histogram bars indicates statistical significance between PFAS-treated groups and the control. Each panel represents results for a different PFAS compound: (<b>a</b>) HFPO-DA, (<b>b</b>) PFMOBA, (<b>c</b>) PFMOPrA, and (<b>d</b>) PFOA. See caption to <a href="#jox-15-00002-f001" class="html-fig">Figure 1</a> for more details.</p>
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<p>Acetylcholinesterase activity. Statistical significance was determined by the Brown–Forsythe ANOVA test, followed by a post hoc Dunnett’s T3 test. * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001; and **** <span class="html-italic">p</span> &lt; 0.0001; ns, not statistically significant. The line above the histogram bars indicates statistical significance between PFAS-treated groups and the control. Each panel represents results for a different PFAS compound: (<b>a</b>) HFPO-DA, (<b>b</b>) PFMOBA, (<b>c</b>) PFMOPrA, and (<b>d</b>) PFOA. See caption to <a href="#jox-15-00002-f001" class="html-fig">Figure 1</a> for more details.</p>
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<p>Escape test. Statistical significance was determined by the Kruskal–Wallis test, followed by a post hoc uncorrected Dunn’s test. * <span class="html-italic">p</span> &lt; 0.05; ns, not statistically significant. The line above the histogram bars indicates statistical significance between PFAS-treated groups and the control. Each panel represents results for a different PFAS compound: (<b>a</b>) HFPO-DA, (<b>b</b>) PFMOBA, (<b>c</b>) PFMOPrA, and (<b>d</b>) PFOA. See caption to <a href="#jox-15-00002-f001" class="html-fig">Figure 1</a> for more details.</p>
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<p>Histogram of latency times obtained from the escape test. The binned counts show the deviations of PFAS specimens from the symmetrical distribution of the control group. A biphasic response is observed, with a general increase in latency times for PFAS treatment. However, PFOA showed quicker median performance as judged by the U-statistics results (see body text).</p>
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42 pages, 3923 KiB  
Review
Environmental Exposure to Per- and Polyfluorylalkyl Substances (PFASs) and Reproductive Outcomes in the General Population: A Systematic Review of Epidemiological Studies
by Alex Haimbaugh, Danielle N. Meyer, Mackenzie L. Connell, Jessica Blount-Pacheco, Dienye Tolofari, Gabrielle Gonzalez, Dayita Banerjee, John Norton, Carol J. Miller and Tracie R. Baker
Int. J. Environ. Res. Public Health 2024, 21(12), 1615; https://doi.org/10.3390/ijerph21121615 - 2 Dec 2024
Viewed by 2143
Abstract
This Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) systematic review synthesized effects of background levels of per- and polyfluorylalkyl substance (PFAS) levels on reproductive health outcomes in the general public: fertility, preterm birth, miscarriage, ovarian health, menstruation, menopause, sperm health, and [...] Read more.
This Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) systematic review synthesized effects of background levels of per- and polyfluorylalkyl substance (PFAS) levels on reproductive health outcomes in the general public: fertility, preterm birth, miscarriage, ovarian health, menstruation, menopause, sperm health, and in utero fetal growth. The inclusion criteria included original research (or primary) studies, human subjects, and investigation of outcomes of interest following non-occupational exposures. It drew from four databases (Web of Science, PubMed, Embase and Health and Environmental Research Online (HERO)) using a standardized search string for all studies published between 1 January 2017 and 13 April 2022. Risk of bias was assessed by two independent reviewers. Data were extracted and reviewed by multiple reviewers. Each study was summarized under its outcome in terms of methodology and results and placed in context, with recommendations for future research. Of 1712 records identified, 30 were eligible, with a total of 27,901 participants (33 datasets, as three studies included multiple outcomes). There was no effect of background levels of PFAS on fertility. There were weakly to moderately increased odds of preterm birth with higher perfluorooctane sulfonic acid (PFOS) levels; the same for miscarriage with perfluorooctanoic acid (PFOA) levels. There was limited yet suggestive evidence for a link between PFAS and early menopause and primary ovarian insufficiency; menstrual cycle characteristics were inconsistent. PFAS moderately increased odds of PCOS- and endometriosis-related infertility, respectively. Sperm motility and DNA health were moderately impaired by multiple PFAS. Fetal growth findings were inconsistent. This review may be used to inform forthcoming drinking water standards and policy initiatives regarding PFAS compounds and drinking water. Future reviews would benefit from more recent studies. Larger studies in these areas are warranted. Future studies should plan large cohorts and open access data availability to capture small effects and serve the public. Funding: Great Lakes Water Authority (Detroit, MI), the Erb Family Foundation through Healthy Urban Waters at Wayne State University (Detroit, MI), and Wayne State University CLEAR Superfund Research (NIH P42ES030991). Full article
(This article belongs to the Section Environmental Health)
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<p>PRISMA flow diagram.</p>
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<p>Pie charts depicting study characteristics. Percentages are rounded to nearest whole number and may not add up to 100%. (<b>a</b>) Number of studies included for each outcome. Some studies measured multiple outcomes. (<b>b</b>) Media type used in studies. Some studies used multiple media. (<b>c</b>) Study design type. (<b>d</b>) Open access status. (<b>e</b>) Region of studies. Scandinavia includes Sweden, Norway, Faroe Islands, and Denmark.</p>
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<p>(<b>a</b>) Participants in each outcome. (<b>b</b>) Average risk of bias scores in each outcome stratified by cohort or cross-sectional studies (left) or case–control (right). Minimum points to be included in the review for cohort/cross-sectional was 7; maximum achievable was 10. Minimum points to be included in the review for case–control was 10; maximum achievable was 14. (<b>c</b>) Median PFAS levels (ng/mL) in each study in maternal/paternal blood, serum, or plasma. Levels reported from controls only in case–control studies. Multi-region studies denoted with (S) for Sweden, (N) for Norway, (Y) for Yantai, and (B) for Beijing. Meng et al., 2018 [<a href="#B58-ijerph-21-01615" class="html-bibr">58</a>]. Sagiv et al., 2017 [<a href="#B59-ijerph-21-01615" class="html-bibr">59</a>]. Liew et al., 2020 [<a href="#B60-ijerph-21-01615" class="html-bibr">60</a>]. Petersen et al., 2018 [<a href="#B61-ijerph-21-01615" class="html-bibr">61</a>]. Ding et al., 2020 [<a href="#B62-ijerph-21-01615" class="html-bibr">62</a>]. Lauritzen et al., 2017 [<a href="#B63-ijerph-21-01615" class="html-bibr">63</a>]. Kalloo et al., 2020 [<a href="#B64-ijerph-21-01615" class="html-bibr">64</a>]. Singer et al., 2018 [<a href="#B65-ijerph-21-01615" class="html-bibr">65</a>]. Zhou et al., 2017 [<a href="#B66-ijerph-21-01615" class="html-bibr">66</a>]. Song et al., 2018 [<a href="#B67-ijerph-21-01615" class="html-bibr">67</a>]. Huo et al., 2020 [<a href="#B68-ijerph-21-01615" class="html-bibr">68</a>]. Pan et al., 2019 [<a href="#B69-ijerph-21-01615" class="html-bibr">69</a>]. Chu et al., 2020 [<a href="#B70-ijerph-21-01615" class="html-bibr">70</a>]. Wang et al., 2021 [<a href="#B71-ijerph-21-01615" class="html-bibr">71</a>]. Wang et al., 2017 [<a href="#B72-ijerph-21-01615" class="html-bibr">72</a>]. Costa et al., 2019 [<a href="#B73-ijerph-21-01615" class="html-bibr">73</a>]. Manzano-Salgado et al., 2017 [<a href="#B74-ijerph-21-01615" class="html-bibr">74</a>]. Zhang et al., 2018 [<a href="#B75-ijerph-21-01615" class="html-bibr">75</a>]. Wikström et al., 2021 [<a href="#B76-ijerph-21-01615" class="html-bibr">76</a>]. Ouidir et al., 2020 [<a href="#B77-ijerph-21-01615" class="html-bibr">77</a>]. Wang et al., 2021 [<a href="#B71-ijerph-21-01615" class="html-bibr">71</a>]. Wise et al., 2022 [<a href="#B78-ijerph-21-01615" class="html-bibr">78</a>]. Wang et al., 2019 [<a href="#B79-ijerph-21-01615" class="html-bibr">79</a>]. Bjorvang et al., 2022 [<a href="#B80-ijerph-21-01615" class="html-bibr">80</a>]. Heffernan et al., 2018 [<a href="#B81-ijerph-21-01615" class="html-bibr">81</a>]. Bjorvang et al., 2021 [<a href="#B82-ijerph-21-01615" class="html-bibr">82</a>]. Eick &amp; Hom Thepaksorn et al., 2020 [<a href="#B83-ijerph-21-01615" class="html-bibr">83</a>]. Liu et al., 2020 [<a href="#B84-ijerph-21-01615" class="html-bibr">84</a>]. Asterisk denotes the concentration of controls in case-control studies, if overall median is not reported.</p>
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<p>Forest plot of odds and risk ratios for preterm birth with increasing PFAS levels from four studies. LNE: line of no effect. PFNA: perfluorononanoic acid. PFHxS: perfluorohexane sulfonic acid. PFOS: perfluorooctane sulfonic acid. PFOA: perflurooctanoic acid. Yang et al., 2022 [<a href="#B108-ijerph-21-01615" class="html-bibr">108</a>]. Sagiv et al., 2018 [<a href="#B59-ijerph-21-01615" class="html-bibr">59</a>]. Manzano-Salgado et al., 2017 [<a href="#B74-ijerph-21-01615" class="html-bibr">74</a>]. Eick &amp; Hom Thepaksorn et al., 2020 [<a href="#B83-ijerph-21-01615" class="html-bibr">83</a>]. Liu et al., 2020 [<a href="#B84-ijerph-21-01615" class="html-bibr">84</a>]. Chu et al., 2020 [<a href="#B70-ijerph-21-01615" class="html-bibr">70</a>].</p>
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<p>Forest plot of miscarriage odds and risk ratios with increasing PFAS levels from three studies. LNE: line of no effect. PFOS: perfluorooctane sulfonic acid. PFOA: perflurooctanoic acid. PFNA: perfluorononanoic acid. PFHxS: perfluorohexane sulfonic acid. PFDA: perfluorodecanoic acid. Wang et al. (2021) results are from Beijing and Yantai sites combined. Wang et al., 2021 [<a href="#B71-ijerph-21-01615" class="html-bibr">71</a>]. Wikström et al., 2021 [<a href="#B76-ijerph-21-01615" class="html-bibr">76</a>]. Liew et al., 2020 [<a href="#B60-ijerph-21-01615" class="html-bibr">60</a>].</p>
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<p>Forest plot of odds and risk ratios of ovarian health effects (top: endometriosis; bottom: PCOS-related infertility) with increasing PFAS levels from two studies. PFHpA: perfluoroheptanoic acid. PFBS: perfluorobutanesulfonic acid. PFDoA: perfluorododecanoic acid. PFHxS: perfluorohexane sulfonic acid. PFUA: perfluoroundecanoic acid. PFDA: perfluorodecanoic acid. PFNA: perfluorononanoic acid. PFOS: perfluorooctanesulfonic acid. PFOA: perfluorooctanoic acid. Wang et al., 2019 [<a href="#B184-ijerph-21-01615" class="html-bibr">184</a>]. Wang et al., 2017 [<a href="#B72-ijerph-21-01615" class="html-bibr">72</a>].</p>
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<p>Forest plot of beta coefficients for sperm health effects with increasing PFAS levels in semen from two studies. Top panel: DNA stability (decreasing); DNA fragmentation index. Bottom panel: sperm motility (% motile sperm). PFDA: perfluorodecanoic acid. PFUnDA: perfluoroundecanoic acid. 6:2 Cl-PFESA: 6:2 chlorinated polyfluorinated ether sulfonate. PFNA: perfluorononanoic acid. PFOS: perfluorooctanesulfonic acid. PFOA: perfluorooctanoic acid. Pan et al., 2019 [<a href="#B69-ijerph-21-01615" class="html-bibr">69</a>]. Petersen et al., 2018 [<a href="#B61-ijerph-21-01615" class="html-bibr">61</a>].</p>
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<p>For each PFAS, median level (ng/mL) across ethnicities (concentrations are compared by column, not row). Color darkens with increasing median concentration. Me-FOSAA: N-methylperfluorooctane sulfonamidoacetic acid. PFDA: perfluorodecanoic acid. PFDoDA: perfluorododecanoic acid. PFHxS: perfluorohexane sulfonate. PFNA: perfluorononanoic acid. PFOA: perfluorooctanoic acid. PFOS: perfluorooctane sulfonate. PFUnDA: perfluoroundecanoic acid.</p>
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<p>Heatmap of beta-values at FDR &lt; 0.05 for measures of fetal growth, broken down by group. Warmer colors (orange, red) indicate lower values, yellow indicates mid-range values, and green indicates higher values. AC: abdominal circumference, FL: femur length, EFW: estimated fetal weight, BD: biparietal diameter. Head circumference not included (not significant for any ethnicity). Me-FOSAA: N-methylperfluorooctane sulfonamidoacetic acid. PFDA: perfluorodecanoic acid. PFDoDA: perfluorododecanoic acid. PFHxS: perfluorohexane sulfonate. PFNA: perfluorononanoic acid. PFOA: perfluorooctanoic acid. PFOS: perfluorooctane sulfonate. PFUnDA: perfluoroundecanoic acid.</p>
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