New Mechanism for the Apoptosis of Human Neuroblastoma Cells by the Interaction between Fluorene-9-Bisphenol and the G Protein-Coupled Estrogen Receptor 1.

Fluorene-9-bisphenol (BHPF) is an emerging contaminant. Presently, there is no report on its interaction with G protein-coupled estrogen receptor 1 (GPER). By using an integrated toxicity research scenario that combined theoretical study with experimental methods, BHPF was found to inhibit the GPER-mediated effect via direct receptor binding. Molecular dynamics simulations found that Trp2726.48 and Glu2756.51 be the key amino acids of BHPF binding with GPER. Moreover, the calculation indicated that BHPF was a suspected GPER inhibitor, which neither can activate GPER nor is able to form water channels of GPER. The role of two residues was successfully verified by following gene knockout and site-directed mutagenesis assays. Further in vitro assays showed that BHPF could attenuate the increase in intracellular concentration of free Ca2+ induced by G1-activated GPER. Besides, BHPF showed an enhanced cytotoxicity compared with G15, indicating that BHPF might be a more potent GPER inhibitor than G15. In addition, a statistically significant effect on the mRNA level of GPER was observed for BHPF. In brief, the present study proposes that BHPF be a GPER inhibitor, and its GPER molecular recognition mechanism has been revealed, which is of great significance for the health risk and assessment of BHPF.

Precisely Identifying the Sources of Magnetic Particles by Hierarchical Classification-Aided Isotopic Fingerprinting.

Magnetic particles (MPs), with magnetite (Fe3O4) and maghemite (γ-Fe2O3) as the most abundant species, are ubiquitously present in the natural environment. MPs are among the most applied engineered particles and can be produced incidentally by various human activities. Identification of the sources of MPs is crucial for their risk assessment and regulation, which, however, is still an unsolved problem. Here, we report a novel approach, hierarchical classification-aided stable isotopic fingerprinting, to address this problem. We found that naturally occurring, incidental, and engineered MPs have distinct Fe and O isotopic fingerprints due to significant Fe/O isotope fractionation during their generation processes, which enables the establishment of an Fe-O isotopic library covering complex sources. Furthermore, we developed a three-level machine learning model that not only can distinguish the sources of MPs with a high precision (94.3%) but also can identify the multiple species (Fe3O4 or γ-Fe2O3) and synthetic routes of engineered MPs with a precision of 81.6%. This work represents the first reliable strategy for the precise source tracing of particles with multiple species and complex sources.

First comprehensive assessment of dietary chlorinated paraffins intake and exposure risk for the rural population of the Tibetan Plateau, China.

Knowledge regarding the occurrence of short-chain and medium-chain chlorinated paraffins (SCCPs and MCCPs) in foodstuffs and their dietary exposure risks for rural Tibetan residents remains largely unknown. Herein, we collected main foodstuffs (including highland barley, vegetables, Tibetan butter, mutton, and yak beef) across the rural Tibetan Plateau and characterized the CP profiles and concentrations. The highest SCCPs concentrations were detected in Tibetan butter (geometric mean (GM): 240.6 ng/g wet weight (ww)), followed by vegetables (59.4 ng/g ww), mutton (51.4 ng/g ww), highland barley (46.3 ng/g ww), and yak beef (31.7 ng/g ww). For MCCPs, the highest concentrations were also detected in Tibetan butter (319.5 ng/g ww), followed by mutton (181.9 ng/g ww), vegetables (127.0 ng/g ww), yak beef (71.2 ng/g ww), and highland barley (30.3 ng/g ww). The predominant congener profiles of SCCPs were C13Cl7-8 in mutton and yak beef, C10Cl7-8 in Tibetan butter, and C10-11Cl6-7 in highland barley and vegetables. The predominant congener profiles of MCCPs were C14Cl7-9 in all sample types. Combined with our previous results of free-range chicken eggs, the median estimated daily intakes (EDIs) of SCCPs and MCCPs via diet for Tibetan rural adults and children was estimated to be 728.8 and 1853.9 ng/kg bw/day and 2565.6 and 5952.8 ng/kg bw/day, respectively. In the worst scenario, MCCPs might induce potential health risks for rural Tibetan population. To our knowledge, this is the first systematic dietary exposure research of SCCPs and MCCPs in the remote rural areas.

First-principles study on the heterogeneous formation of environmentally persistent free radicals (EPFRs) over α-Fe2O3(0001) surface: Effect of oxygen vacancy.

Metal oxides with oxygen vacancies have a significant impact on catalytic activity for the transformation of organic pollutants in waste-to-energy (WtE) incineration processes. This study aims to investigate the influence of hematite surface oxygen point defects on the formation of environmentally persistent free radicals (EPFRs) from phenolic compounds based on the first-principles calculations. Two oxygen-deficient conditions were considered: oxygen vacancies at the top surface and on the subsurface. Our simulations indicate that the adsorption strength of phenol on the α-Fe2O3(0001) surface is enhanced by the presence of oxygen vacancies. However, the presence of oxygen vacancies has a negative impact on the dissociation of the phenol molecule, particularly for the surface with a defective point at the top layer. Thermo-kinetic parameters were established over a temperature range of 300-1000 K, and lower reaction rate constants were observed for the scission of phenolic O-H bonds over the oxygen-deficient surfaces compared to the pristine surface. The negative effects caused by the oxygen-deficient conditions could be attributed to the local reduction of FeIII to FeII, which lower the oxidizing ability of surface reaction sites. The findings of this study provide us a promising approach to regulate the formation of EPFRs.

Remarkable Contamination of Short- and Medium-Chain Chlorinated Paraffins in Free-Range Chicken Eggs from Rural Tibetan Plateau.

Rapid social-economic development introduces modern lifestyles into rural areas, not only bringing numerous modern products but also new pollutants, such as chlorinated paraffins (CPs). The rural Tibetan Plateau has limited industrial activities and is a unique place to investigate this issue. Herein we collected 90 free-range chicken egg pool samples across the rural Tibetan Plateau to evaluate the pollution status of CPs. Meanwhile, CPs in related soils, free-range chicken eggs from Jiangxi, and farmed eggs from markets were also analyzed. The median concentrations of SCCPs (159 ng g-1 wet weight (ww)) and MCCPs (1390 ng g-1 ww) in Tibetan free-range chicken eggs were comparable to those from Jiangxi (259 and 938 ng g-1 ww) and significantly higher than those in farmed eggs (22.0 and 81.7 ng g-1 ww). In the rural Tibetan Plateau, the median EDI of CPs via egg consumption by adults and children were estimated to be 81.6 and 220.2 ng kg-1 bw day-1 for SCCPs and 483.4 and 1291 ng kg-1 bw day-1 for MCCPs, respectively. MCCPs might pose potential health risks for both adults and children in the worst scenario. Our study demonstrates that new pollutants should not be ignored and need further attention in remote rural areas.

Typical neonicotinoids and organophosphate esters, but not their metabolites, adversely impact early human development by activating BMP4 signaling.

Recent studies have highlighted the presence of potentially harmful chemicals, such as neonicotinoids (NEOs) and organophosphate esters (OPEs), in everyday items. Despite their potential threats to human health, these dangers are often overlooked. In a previous study, we discovered that NEOs and OPEs can negatively impact development, but liver metabolism can help mitigate their harmful effects. In our current research, our objective was to investigate the toxicity mechanisms associated with NEOs, OPEs, and their liver metabolites using a human embryonic stem cell-based differentiation model that mimics early embryonic development. Our transcriptomics data revealed that NEOs and OPEs significantly influenced the expression of hundreds of genes, disrupted around 100 biological processes, and affected two signaling pathways. Notably, the BMP4 signaling pathway emerged as a key player in the disruption caused by exposure to these pollutants. Both NEOs and OPEs activated BMP4 signaling, potentially impacting early embryonic development. Interestingly, we observed that treatment with a human liver S9 fraction, which mimics liver metabolism, effectively reduced the toxic effects of these pollutants. Most importantly, it reversed the adverse effects dependent on the BMP4 pathway. These findings suggest that normal liver function plays a crucial role in detoxifying environmental pollutants and provides valuable experimental insights for addressing this issue.

Deciphering exogenous chemical carcinogenicity through interpretable deep learning: A novel approach for evaluating atmospheric pollutant hazards.

Cancer remains a significant global health concern, with millions of deaths attributed to it annually. Environmental pollutants play a pivotal role in cancer etiology and contribute to the growing prevalence of this disease. The carcinogenic assessment of these pollutants is crucial for chemical health evaluation and environmental risk assessments. Traditional experimental methods are expensive and time-consuming, prompting the development of alternative approaches such as in silico methods. In this regard, deep learning (DL) has shown potential but lacks optimal performance and interpretability. This study introduces an interpretable DL model called CarcGC for chemical carcinogenicity prediction, utilizing a graph convolutional neural network (GCN) that employs molecular structural graphs as inputs. Compared to existing models, CarcGC demonstrated enhanced performance, with the area under the receiver operating characteristic curve (AUCROC) reaching 0.808 on the test set. Due to air pollution is closely related to the incidence of lung cancers, we applied the CarcGC to predict the potential carcinogenicity of chemicals listed in the United States Environmental Protection Agency's Hazardous Air Pollutants (HAPs) inventory, offering a foundation for environmental carcinogenicity screening. This study highlights the potential of artificially intelligent methods in carcinogenicity prediction and underscores the value of CarcGC interpretability in revealing the structural basis and molecular mechanisms underlying chemical carcinogenicity.

CatNet: Sequence-based deep learning with cross-attention mechanism for identifying endocrine-disrupting chemicals.

Endocrine-disrupting chemicals (EDCs) pose significant environmental and health risks due to their potential to interfere with nuclear receptors (NRs), key regulators of physiological processes. Despite the evident risks, the majority of existing research narrows its focus on the interaction between compounds and the individual NR target, neglecting a comprehensive assessment across the entire NR family. In response, this study assembled a comprehensive human NR dataset, capturing 49,244 interactions between 35,467 unique compounds and 42 NRs. We introduced a cross-attention network framework, "CatNet", innovatively integrating compound and protein representations through cross-attention mechanisms. The results showed that CatNet model achieved excellent performance with an area under the receiver operating characteristic curve (AUCROC) = 0.916 on the test set, and exhibited reliable generalization on unseen compound-NR pairs. A distinguishing feature of our research is its capacity to expand to novel targets. Beyond its predictive accuracy, CatNet offers a valuable mechanistic perspective on compound-NR interactions through feature visualization. Augmenting the utility of our research, we have also developed a graphical user interface, empowering researchers to predict chemical binding to diverse NRs. Our model enables the prediction of human NR-related EDCs and shows the potential to identify EDCs related to other targets.

Dynamic Behavior and Interaction Mechanism of Soil Organic Matter in Water Systems: A Coarse-Grained Molecular Dynamics Study.

Investigating soil organic matter's (SOM) microscale assembly and functionality is challenging due to its complexity. This study constructs comparatively realistic SOM models, including diverse components such as Leonardite humic acid (LHA), lipids, peptides, carbohydrates, and lignin, to unveil their spontaneous self-assembly behavior at the mesoscopic scale through microsecond coarse-grained molecular dynamics simulations. We discovered an ordered SOM aggregation creating a layered phase from its hydrophobic core to the aqueous phase, resulting in an increasing O/C ratio and declining structural amphiphilicity. Notably, the amphiphilic lipids formed a bilayer membrane, partnering with lignin to constitute SOM's hydrophobic core. LHA, despite forming a layer, was embedded within this structure. The formation of such complex architectures was driven by nonbonded interactions between components. Our analysis revealed component-dependent diffusion effects within the SOM system. Lipids, peptides, and lignin showed inhibitory effects on self-diffusion, while carbohydrates facilitated diffusion. This study offers novel insights into the dynamic behavior and assembly of SOM components, introducing an effective approach for studying dynamic SOM mechanisms in aquatic environments.

Multiscale computational simulation of pollutant behavior at water interfaces.

The investigation of pollutant behavior at water interfaces is critical to understand pollution in aquatic systems. Computational methods allow us to overcome the limitations of experimental analysis, delivering valuable insights into the chemical mechanisms and structural characteristics of pollutant behavior at interfaces across a range of scales, from microscopic to mesoscopic. Quantum mechanics, all-atom molecular dynamics simulations, coarse-grained molecular dynamics simulations, and dissipative particle dynamics simulations represent diverse molecular interaction calculation methods that can effectively model pollutant behavior at environmental interfaces from atomic to mesoscopic scales. These methods provide a rich variety of information on pollutant interactions with water surfaces. This review synthesizes the advancements in applying typical computational methods to the formation, adsorption, binding, and catalytic conversion of pollutants at water interfaces. By drawing on recent advancements, we critically examine the current challenges and offer our perspective on future directions. This review seeks to advance our understanding of computational techniques for elucidating pollutant behavior at water interfaces, a critical aspect of water research.

Linking Transthyretin-Binding Chemicals and Free Thyroid Hormones: In Vitro to In Vivo Extrapolation Based on a Competitive Binding Model.

Large numbers of pollutants competitively inhibit the binding between thyroid hormones and transthyretin (TTR) in vitro. However, the impact of this unintended binding on free thyroid hormones in vivo has not yet been characterized. Herein, we established a quantitative in vitro to in vivo extrapolation (QIVIVE) method based on a competitive binding model to quantify the effect of TTR-binding chemicals on free thyroid hormones in human blood. Twenty-five TTR-binding chemicals including 6 hydroxyl polybromodiphenyl ethers (OH-PDBEs), 6 hydroxyl polychlorobiphenyls (OH-PCBs), 4 halogenphenols, 5 per- and polyfluorinated substances (PFASs), and 4 phenols were selected for investigation. Incorporating the in vitro binding parameters and human exposure data, the QIVIVE model could well predict the in vivo effect on free thyroid hormones. Co-exposure to twenty-five typical TTR-binding chemicals resulted in median increases of 0.080 and 0.060% in circulating levels of free thyroxine (FT4) and free triiodothyronine (FT3) in the general population. Individuals with occupational exposure to TTR-binding chemicals suffered 1.88-32.2% increases in free thyroid hormone levels. This study provides a quantitative tool to evaluate the thyroid-disrupting risks of TTR-binding chemicals and proposes a new framework for assessing the in vivo effects of chemical exposures on endogenous molecules.

Machine learning strategy on activation energy of environmental heterogeneous reactions and its application to atmospheric formation of typical montmorillonite-bound phenoxy radicals.

Heterogeneous transformation of organic pollutants into more toxic chemicals poses substantial health risks to humans. Activation energy is an important indicator that help us to understand transformation efficacy of environmental interfacial reactions. However, the determination of activation energies for large numbers of pollutants using either the experimental or high-accuracy theoretical methods is expensive and time-consuming. Alternatively, the machine learning (ML) method shows the strength in predictive performance. In this study, using the formation of a typical montmorillonite-bound phenoxy radical as an example, a generalized ML framework RAPID was proposed for activation energy prediction of environmental interfacial reactions. Accordingly, an explainable ML model was developed to predict the activation energy via easily accessible properties of the cations and organics. The model developed by decision tree (DT) performed best with the lowest root-mean-squared error (RMSE = 0.22) and the highest coefficient of determination values (R2 score = 0.93), the underlying logic of which was well understood by combining model visualization and SHapley Additive exPlanations (SHAP) analysis. The performance and interpretability of the established model suggest that activation energies can be predicted by the well-designed ML strategy, and this would allow us to predict more heterogeneous transformation reactions in the environmental field.

Machine learning-aided metallomic profiling in serum and urine of thyroid cancer patients and its environmental implications.

The incidence rate of thyroid cancer has been growing worldwide. Thyroid health is closely related with multiple trace metals, and the nutrients are essential in maintaining thyroid function while the contaminants can disturb thyroid morphology and homeostasis. In this study, we conducted metallomic analysis in thyroid cancer patients (n = 40) and control subjects (n = 40) recruited in Shenzhen, China with a high incidence of thyroid cancer. We found significant alterations in serumal and urinary metallomic profiling (including Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Sr, Cd, I, Ba, Tl, and Pb) and elemental correlative patterns between thyroid cancer patients and controls. Additionally, we also measured the serum Cu isotopic composition and found a multifaceted disturbance in Cu metabolism in thyroid disease patients. Based on the metallome variations, we built and assessed the thyroid cancer-predictive performance of seven machine learning algorithms. Among them, the Random Forest model performed the best with the accuracy of 1.000, 0.858, and 0.813 on the training, 5-fold cross-validation, and test set, respectively. The high performance of machine learning has demonstrated the great promise of metallomic analysis in the identification of thyroid cancer. Then, the Shapley Additive exPlanations approach was used to further interpret the variable contributions of the model and it showed that serum Pb contributed the most in the identification process. To the best of our knowledge, this is the first study that combines machine learning and metallome data for cancer identification, and it supports the indication of environmental heavy metal-related thyroid cancer etiology.

Occurrence and risk assessment of organophosphate esters in global aquatic products.

Organophosphate esters (OPEs), as an important class of new pollutants, have been pervasively detected in global aquatic products, arousing widespread public concern due to their potential bioaccumulative behavior and consequent risks. With the continuous improvement of living standards of citizens, there have been constant increment of the proportion of aquatic products in diets of people. The levels of OPEs exposed to residents may also be rising due to the augmented consumption of aquatic products, posing potential hazards on human health, especially for people in coastal areas. The present study integrated the concentrations, profiles, bioaccumulation, and trophic transfer of OPEs in global aquatic products, including Mollusca, Crustacea, and fish, evaluated health risks of OPEs through aquatic products in daily diets by Mont Carol Simulation (MCS), and found Asia has been the most polluted area in terms of the concentration of OPEs in aquatic products, and would have been increasingly polluted. Among all studied OPEs, chlorinated OPEs generally showed accumulation predominance. It is worth noting that some OPEs were found bioaccumulated and/or biomagnified in aquatic ecosystems. Though MCS revealed relative low exposure risks of residents, sensitive and special groups such as children, adolescents, and fishermen may face more serious health risks than the average residents. Finally, knowledge gaps and recommendations for future research are discussed encouraging more long-term and systematic global monitoring, comprehensive studies of novel OPEs and OPEs metabolites, and more toxicological studies to completely evaluate the potential risks of OPEs.

Exogenous Chemicals Impact Virus Receptor Gene Transcription: Insights from Deep Learning.

Despite the fact that coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been disrupting human life and health worldwide since the outbreak in late 2019, the impact of exogenous substance exposure on the viral infection remains unclear. It is well-known that, during viral infection, organism receptors play a significant role in mediating the entry of viruses to enter host cells. A major receptor of SARS-CoV-2 is the angiotensin-converting enzyme 2 (ACE2). This study proposes a deep learning model based on the graph convolutional network (GCN) that enables, for the first time, the prediction of exogenous substances that affect the transcriptional expression of the ACE2 gene. It outperforms other machine learning models, achieving an area under receiver operating characteristic curve (AUROC) of 0.712 and 0.703 on the validation and internal test set, respectively. In addition, quantitative polymerase chain reaction (qPCR) experiments provided additional supporting evidence for indoor air pollutants identified by the GCN model. More broadly, the proposed methodology can be applied to predict the effect of environmental chemicals on the gene transcription of other virus receptors as well. In contrast to typical deep learning models that are of black box nature, we further highlight the interpretability of the proposed GCN model and how it facilitates deeper understanding of gene change at the structural level.

Metallomic profiling and natural copper isotopic signatures of childhood autism in serum and red blood cells.

Excessive exposure to metals directly threatens human health, including neurodeve lopment. Autism spectrum disorder (ASD) is a neurodevelopmental disorder, leaving great harms to children themselves, their families, and even society. In view of this, it is critical to develop reliable biomarkers for ASD in early childhood. Here we used inductively coupled plasma mass spectrometry (ICP-MS) to identify the abnormalities in ASD-associated metal elements in children blood. Multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS) was applied to detect isotopic differences in copper (Cu) for further assessment on account of its core role in the brain. We also developed a machine learning classification method for unknown samples based on a support vector machine (SVM) algorithm. The results indicated significant differences in the blood metallome (chromium (Cr), manganese (Mn), cobalt (Co), magnesium (Mg), and arsenic (As)) between cases and controls, and a significantly lower Zn/Cu ratio was observed in the ASD cases. Interestingly, we found a strong association of serum copper isotopic composition (δ65Cu) with autistic serum. SVM was successfully applied to discriminate cases and controls based on the two-dimensional Cu signatures (Cu concentration and δ65Cu) with a high accuracy (94.4%). Overall, our findings revealed a new biomarker for potential early diagnosis and screening of ASD, and the significant alterations in the blood metallome also helped to understand the potential pathogenesis of ASD in terms of metallomics.

Spectroscopic fingerprints profiling the polysaccharide/protein/humic architecture of stratified extracellular polymeric substances (EPS) in activated sludge.

Extracellular polymeric substances (EPS), with a stratified structure including tightly-bound EPS (TB-EPS), loosely-bound EPS (LB-EPS), and soluble EPS (S-EPS) surrounding the microbial cells, are known to vitally affect the physicochemical and biological functions of activated sludge in wastewater treatment. Polysaccharides (PS), proteins (PN), and humic acids (HA) are key components of EPS but their roles in constructing the multi-layer architecture are still unclear. This study explored the EPS characteristics in relation to the components using spectroscopic fingerprinting techniques. Ultraviolet-visible (UV-vis) spectra demonstrated stark difference between TB-EPS and other EPS. Fluorescence excitation-emission matrix (FEEM) and apparent quantum yield revealed further detailed differences. Fluorescence quotient analysis highlighted the dominance of TB-EPS, LB-EPS, and S-EPS in the excitation/emission wavelength (Ex/Em) region of Em = 350-400 nm, Em > 400 nm, and low-Stokes shift band (Em - Ex < 25 nm), respectively. Wavelength-wise prediction of the FEEM intensity was achieved through multiple linear regression against the chemical composition and variance partitioning analysis witnessed binary interactions of PS×HA and PS×PN in S-EPS, PN×HA and PS×PN in LB-EPS, and ternary interaction of PS×PN×HA in TB-EPS as well as the wavelength-specific fluorescence responses of these interactions. Further, X-ray photoelectron spectroscopy, infrared spectra, and circular dichroism spectra corroborated the differences in primary, secondary, and tertiary structures across the EPS layers. Ultrahigh-performance liquid chromatography-mass spectrometry detected molecular fragments confirming the multi-component hybridization among PS, PN, and HA. This study demonstrates a spectroscopic approach to sensitively fingerprint the fine structure of EPS, which has the potential for rapid monitoring of EPS and related sludge properties in wastewater treatment systems.

Candida albicans-induced activation of the TGF-ß/Smad pathway and upregulation of IL-6 may contribute to intrauterine adhesion.

Iatrogenic injury to endometrial tissue is the main cause of intrauterine adhesions (IUA) and infection can also damage the endometrium. The microbiota plays an important role in the health of the female reproductive tract. However, the mechanism is still unclear. In total, 908 patients with IUA and 11,389 healthy individuals were retrospectively selected for this clinical study. Participant information including vaginal microecological results and human papillomavirus (HPV) status were collected. Univariate and multivariate logistic regression analyses were used to identify the factors related to IUA. Next, animal experiments were performed in a curettage-induced IUA rat model. After the procedure, rats in the experimental group received a vaginal infusion of a Candida albicans (C. albicans) fungal solution. On days 3, 7, and 14 after curettage and infusion, the expression levels of IL-6, fibrotic pathway-related factors (TGF-ß1, Smad 2, and COL1), and estrogen receptor (ER) and progesterone receptor (PR) in rat endometrial tissues were assessed. Fungal infection of the reproductive tract was found to be an independent risk factor for IUA (P < 0.05). The inflammatory response and degree of fibrosis were greater in rats infected with C. albicans than in the controls. The levels of IL-6, TGF-ß1, Smad 2, and COL1 expression in endometrial tissues were significantly higher in the experimental group than in the control group (P < 0.05). However, the ER and PR levels were lower in the IUA group than in the non-IUA group (P < 0.05). C. albicans infection may be related to IUA. C. albicans elicits a strong inflammatory response that can lead to more severe endometrial fibrosis.

Source Identification of Organophosphate Esters through the Profiles in Proglacial and Ocean Sediments from Ny-Ålesund, the Arctic.

Little is known about the sources and environmental behavior of organophosphate esters (OPEs) in the Arctic, especially their transformation products. The present study unprecedentedly investigated both 16 tri-OPEs and 8 di-OPEs in proglacial and ocean sediments from Ny-Ålesund, the Arctic. Mean concentrations of tri-OPEs and di-OPEs in proglacial sediments were 487 and 341 pg/g dry weight (dw), respectively, which were significantly lower than those in ocean sediments (1692 and 525 pg/g dw). Ocean sediments might be simultaneously influenced by long-range atmospheric transport (LRAT), oceanic transport, and human activities, whereas proglacial sediments, since they are isolated from human settlements, may be dominantly affected by LRAT. Such source difference was evidenced by the contamination profile of OPEs: chlorinated tri-OPEs with high environmental persistence and high LRAT were dominant in proglacial sediments (66%); however, weakly environmentally persistent and highly hydrophobic aryl tri-OPEs were dominant in ocean sediments (47%), which were plausibly from local emission sources due to their low LRAT potential. Di-OPEs in proglacial and ocean sediments were dominated by groups of parent tri-OPEs with strong photodegradability, such as alkyl (75%) and aryl (58%). A higher mean molar ratio of di-OPE/tri-OPE in the proglacial sediment (14) than that in the ocean sediment (2.2) may be related to its higher photodegradation than that of the ocean sediment.