Soil's Hidden Power: The Stable Soil Organic Carbon Pool Controls the Burden of Persistent Organic Pollutants in Background Soils.

Persistent organic pollutants (POPs) tend to accumulate in cold regions by cold condensation and global distillation. Soil organic matter is the main storage compartment for POPs in terrestrial ecosystems due to deposition and repeated air-surface exchange processes. Here, physicochemical properties and environmental factors were investigated for their role in influencing POPs accumulation in soils of the Tibetan Plateau and Antarctic and Arctic regions. The results showed that the soil burden of most POPs was closely coupled to stable mineral-associated organic carbon (MAOC). Combining the proportion of MAOC and physicochemical properties can explain much of the soil distribution characteristics of the POPs. The background levels of POPs were estimated in conjunction with the global soil database. It led to the proposition that the stable soil carbon pools are key controlling factors affecting the ultimate global distribution of POPs, so that the dynamic cycling of soil carbon acts to counteract the cold-trapping effects. In the future, soil carbon pool composition should be fully considered in a multimedia environmental model of POPs, and the risk of secondary release of POPs in soils under conditions such as climate change can be further assessed with soil organic carbon models.

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.

Bioaccumulation and Trophodynamics of Novel Brominated Flame Retardants (NBFRs) in Marine Food Webs from the Arctic and Antarctic Regions.

The pervasive contamination of novel brominated flame retardants (NBFRs) in remote polar ecosystems has attracted great attention in recent research. However, understanding regarding the trophic transfer behavior of NBFRs in the Arctic and Antarctic marine food webs is limited. In this study, we examined the occurrence and trophodynamics of NBFRs in polar benthic marine sediment and food webs collected from areas around the Chinese Arctic Yellow River Station (n = 57) and Antarctic Great Wall Station (n = 94). ∑7NBFR concentrations were in the range of 1.27-7.47 ng/g lipid weight (lw) and 0.09-1.56 ng/g lw in the Arctic and Antarctic marine biota, respectively, among which decabromodiphenyl ethane (DBDPE) was the predominant compound in all sample types. The biota-sediment bioaccumulation factors (g total organic carbon/g lipid) of NBFRs in the Arctic (0.85-3.40) were 4-fold higher than those in the Antarctica (0.13-0.61). Trophic magnification factors (TMFs) and their 95% confidence interval (95% CI) of individual NBFRs ranged from 0.43 (95% CI: 0.32, 0.60) to 1.32 (0.92, 1.89) and from 0.34 (0.24, 0.49) to 0.92 (0.56, 1.51) in the Arctic and Antarctic marine food webs, respectively. The TMFs of most congeners were significantly lower than 1, indicating a trophic dilution potential. This is one of the very few investigations on the trophic transfer of NBFRs in remote Arctic and Antarctic marine ecosystems, which provides a basis for exploring the ecological risks of NBFRs in polar regions.

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.

Response of ocean acidification to atmospheric carbon dioxide removal.

Artificial CO2 removal from the atmosphere (also referred to as negative CO2 emissions) has been proposed as a potential means to counteract anthropogenic climate change. Here we use an Earth system model to examine the response of ocean acidification to idealized atmospheric CO2 removal scenarios. In our simulations, atmospheric CO2 is assumed to increase at a rate of 1% per year to four times its pre-industrial value and then decreases to the pre-industrial level at a rate of 0.5%, 1%, 2% per year, respectively. Our results show that the annual mean state of surface ocean carbonate chemistry fields including hydrogen ion concentration ([H+]), pH and aragonite saturation state respond quickly to removal of atmospheric CO2. However, the change of seasonal cycle in carbonate chemistry lags behind the decline in atmospheric CO2. When CO2 returns to the pre-industrial level, over some parts of the ocean, relative to the pre-industrial state, the seasonal amplitude of carbonate chemistry fields is substantially larger. Simulation results also show that changes in deep ocean carbonate chemistry substantially lag behind atmospheric CO2 change. When CO2 returns to its pre-industrial value, the whole-ocean acidity measured by [H+] is 15%-18% larger than the pre-industrial level, depending on the rate of CO2 decrease. Our study demonstrates that even if atmospheric CO2 can be lowered in the future as a result of net negative CO2 emissions, the recovery of some aspects of ocean acidification would take decades to centuries, which would have important implications for the resilience of marine ecosystems.

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.

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.

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.

Foraging behavior and sea ice-dependent factors affecting the bioaccumulation of mercury in Antarctic coastal waters.

To elucidate the potential risks of the toxic pollutant mercury (Hg) in polar waters, the study of accumulated Hg in fish is compelling for understanding the cycling and fate of Hg on a regional scale in Antarctica. Herein, the Hg isotopic compositions of Antarctic cod Notothenia coriiceps were assessed in skeletal muscle, liver, and heart tissues to distinguish the differences in Hg accumulation in isolated coastal environments of the eastern (Chinese Zhongshan Station, ZSS) and the antipode western Antarctica (Chinese Great Wall Station, GWS), which are separated by over 4000 km. Differences in odd mass-independent isotope fractionation (odd-MIF) and mass-dependent fractionation (MDF) across fish tissues were reflection of the specific accumulation of methylmercury (MeHg) and inorganic Hg (iHg) with different isotopic fingerprints. Internal metabolism including hepatic detoxification and processes related to heart may also contribute to MDF. Regional heterogeneity in iHg end-members further provided evidence that bioaccumulated Hg origins can be largely influenced by polar water circumstances and foraging behavior. Sea ice was hypothesized to play critical roles in both the release of Hg with negative odd-MIF derived from photoreduction of Hg2+ on its surface and the impediment of photochemical transformation of Hg in water layers. Overall, the multitissue isotopic compositions in local fish species and prime drivers of the heterogeneous Hg cycling and bioaccumulation patterns presented here enable a comprehensive understanding of Hg biogeochemical cycling in polar coastal waters.

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.

Assessing the occurrence and sources of synthetic additive pollutants in lake sediments using fecal and sewage markers.

Benzotriazole ultraviolet stabilizers (BZT-UVs), substituted diphenylamine antioxidants (S-DPAs), and synthetic phenolic antioxidants (SPAs) are three types of synthetic additive pollutants that are received increasing attention in the recent decade. In this study, a total of twenty-seven target analytes were measured in sediment cores and surface sediments collected from a lake with long sedimentary history. All target analytes were detected in the sediment samples, and mean values of the total ∑BZT-UVs, ∑S-DPAs and ∑SPAs concentrations were 220 ± 552 ng/g dry weight (d.w.), 20.8 ± 27.9 ng/g d. w., and 95.8 ± 146 ng/g d. w., respectively. Based on the dating results of two sediment cores, the total concentrations of BZT-UVs, S-DPA, and SPAs showed a fluctuating but overall increasing trend over time, which intuitively reflects the change of historical emissions. Meanwhile, fecal and sewage markers (i.e., sterols and pharmaceuticals, respectively) were jointly used to trace the probable sources of these pollutants. Results from the correlation and clustering analyses suggest that unregular fecal discharges and point-source domestic sewage need to be considered if pollution in the investigated area is to be controlled. To our knowledge, this study is the initial attempt to reveal the temporal variations of these synthetic additive pollutants in an aquatic environment in China and to demonstrate the feasibility of using markers to trace the sources of emerging pollutant analogues.

Optimizing the physiological pharmacokinetic model to rank the risks of persistent organic pollutants towards fish on the Tibetan Plateau.

Persistent organic pollutants (POPs) could pose adverse risks towards fish in aquatic environments. However, related risk assessments in remote regions are lacking. In this study we investigated three kinds of POPs in four common fish species (n = 62) from high-altitude rivers and lakes on the Tibetan Plateau. The results showed that the lipid weight concentrations of organochlorine pesticides (OCPs), polycyclic aromatic hydrocarbons (PAHs), and perfluoroalkyl substances (PFAS) in fish muscle followed the order Σ13PAHs (24.5-3354 ng/g) > Σ11PFAS (2.48-164 ng/g) > Σ7OCPs (1.61-82.2 ng/g), which is comparable to that found in other remote regions. The physiologically based pharmacokinetic (PBPK) model was optimized using physiological parameters specific to the sampled Tibetan fish to generate accurate effective concentration (EC) thresholds. Based on these measured concentrations and newly simulated EC thresholds, the ecological risk ratios for selected toxic POPs (dichlorodiphenyltrichloroethane (DDT), pyrene (Pyr), and perfluorooctane sulfonate (PFOS)) ranged from 8.53 × 10-8 to 2.03 × 10-5. Racoma tibetanus and Schizothorax macropogon were the most vulnerable Tibetan fish species. All the risk ratios were far below 1, indicating that there was no risk of POPs towards Tibetan fish. However, the risk ratios for emerging POPs (i.e., PFOS) were 2-3 orders of magnitude higher than for legacy POPs (i.e., DDT and Pyr), suggesting that monitoring of emerging POPs should be reinforced. Our study sheds light on the risk assessment of wildlife exposed to POPs in remote regions with limited toxicity data.

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.

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.

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.

Disposable Polypropylene Face Masks: A Potential Source of Micro/Nanoparticles and Organic Contaminates in Humans.

We have been effectively protected by disposable propylene face masks during the COVID-19 pandemic; however, they may pose health risks due to the release of fine particles and chemicals. We measured micro/nanoparticles and organic chemicals in disposable medical masks, surgical masks, and (K)N95 respirators. In the breathing-simulation experiment, no notable differences were found in the total number of particles among mask types or between breathing intensities. However, when considering subranges, <2.5 µm particles accounted for ∼90% of the total number of micro/nanoparticles. GC-HRMS-based suspect screening tentatively revealed 79 (semi)volatile organic compounds in masks, with 18 being detected in ≥80% of samples and 44 in ≤20% of samples. Three synthetic phenolic antioxidants were quantified, and AO168 reached a median concentration of 2968 ng/g. By screening particles collected from bulk mask fabrics, we detected 18 chemicals, including four commonly detected in masks, suggesting chemical partition between the particles and the fabric fibers and chemical exposure via particle inhalation. These particles and chemicals are believed to originate from raw materials, intentionally and nonintentionally added substances in mask production, and their transformation products. This study highlights the need to study the long-term health risks associated with mask wearing and raises concerns over mask quality control.

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.