Modeling the Mercury Cycle in the Sea Ice Environment: A Buffer between the Polar Atmosphere and Ocean.

Sea ice (including overlying snow) is a dynamic interface between the atmosphere and the ocean, influencing the mercury (Hg) cycling in polar oceans. However, a large-scale and process-based model for the Hg cycle in the sea ice environment is lacking, hampering our understanding of regional Hg budget and critical processes. Here, we develop a comprehensive model for the Hg cycle at the ocean-sea ice-atmosphere interface with constraints from observational polar cryospheric data. We find that seasonal patterns of average total Hg (THg) in snow are governed by snow thermodynamics and deposition, peaking in springtime (Arctic: 5.9 ng/L; Antarctic: 5.3 ng/L) and minimizing during ice formation (Arctic: 1.0 ng/L, Antarctic: 0.5 ng/L). Arctic and Antarctic sea ice exhibited THg concentration peaks in summer (0.25 ng/L) and spring (0.28 ng/L), respectively, governed by different snow Hg transmission pathways. Antarctic snow-ice formation facilitates Hg transfer to sea ice during spring, while in the Arctic, snow Hg is primarily moved through snowmelt. Overall, first-year sea ice acts as a buffer, receiving atmospheric Hg during ice growth and releasing it to the ocean in summer, influencing polar atmospheric and seawater Hg concentrations. Our model can assess climate change effects on polar Hg cycles and evaluate the Minamata Convention's effectiveness for Arctic populations.

Tissue composition and storage duration affect the usefulness of generic wet-to-dry mass conversion factors in toxicology studies.

All ecosystems are exposed to a variety of anthropogenic contaminants. The potential threat posed by these contaminants to organisms has prompted scores of toxicology studies. Contaminant concentrations in wildlife toxicology studies are inconsistently expressed in wet or dry mass units, or even on a lipid-normalized basis, but tissue composition is rarely reported, and the conversion between dry and wet mass units, notably, is often based on assumed empirical moisture contents in tissues. However, diverse factors (e.g., tissue, storage conditions) may affect tissue composition and render comparisons between studies difficult or potentially biased. Here, we used data on the concentration of mercury, a global pollutant, in tissues of red foxes (Vulpes vulpes) to quantify the effects of diverse variables on moisture and lipid contents, and their consequences on contaminant concentration in different tissues, when converting between wet and dry mass units (lipid extracted or not). We found that moisture content differed largely between organs, enough to preclude the use of a single conversion factor, and decreased by 1% per year when stored at -80 °C. Although most fox tissues had low lipid concentrations, lipid content affected water content and their extraction affected the wet to dry mass conversion factor. We thus recommend reporting tissue composition (at least water and lipid contents) systematically in toxicology studies of mercury specifically and of contaminants in general, and using tissue/species specific conversion factors to convert between dry and wet mass concentration.

Global warming accelerates uptake of atmospheric mercury in regions experiencing glacier retreat.

As global climate continues to warm, melting of glaciers releases a large quantity of mercury (Hg) originally locked in ice into the atmosphere and downstream ecosystems. Here, we show an opposite process that captures atmospheric Hg through glacier-to-vegetation succession. Our study using stable isotope techniques at 3 succession sites on the Tibetan Plateau reveals that evolving vegetation serves as an active "pump" to take up gaseous elemental mercury (Hg0) from the atmosphere. The accelerated uptake enriches the Hg pool size in glacier-retreated areas by a factor of ∼10 compared with the original pool size in the glacier. Through an assessment of Hg source-sink relationship observed in documented glacier-retreated areas in the world (7 sites of tundra/steppe succession and 5 sites of forest succession), we estimate that 400 to 600 Mg of Hg has been accumulated in glacier-retreated areas (5‰ of the global land surface) since the Little Ice Age (∼1850). By 2100, an additional ∼300 Mg of Hg will be sequestered from the atmosphere in glacier-retreated regions globally, which is ∼3 times the total Hg mass loss by meltwater efflux (∼95 Mg) in alpine and subpolar glacier regions. The recapturing of atmospheric Hg by vegetation in glacier-retreated areas is not accounted for in current global Hg models. Similar processes are likely to occur in other regions that experience increased vegetation due to climate or land use changes, which need to be considered in the assessment of global Hg cycling.

Photochemistry of oxidized Hg(I) and Hg(II) species suggests missing mercury oxidation in the troposphere.

Mercury (Hg), a global contaminant, is emitted mainly in its elemental form Hg0 to the atmosphere where it is oxidized to reactive HgII compounds, which efficiently deposit to surface ecosystems. Therefore, the chemical cycling between the elemental and oxidized Hg forms in the atmosphere determines the scale and geographical pattern of global Hg deposition. Recent advances in the photochemistry of gas-phase oxidized HgI and HgII species postulate their photodissociation back to Hg0 as a crucial step in the atmospheric Hg redox cycle. However, the significance of these photodissociation mechanisms on atmospheric Hg chemistry, lifetime, and surface deposition remains uncertain. Here we implement a comprehensive and quantitative mechanism of the photochemical and thermal atmospheric reactions between Hg0, HgI, and HgII species in a global model and evaluate the results against atmospheric Hg observations. We find that the photochemistry of HgI and HgII leads to insufficient Hg oxidation globally. The combined efficient photoreduction of HgI and HgII to Hg0 competes with thermal oxidation of Hg0, resulting in a large model overestimation of 99% of measured Hg0 and underestimation of 51% of oxidized Hg and ∼66% of HgII wet deposition. This in turn leads to a significant increase in the calculated global atmospheric Hg lifetime of 20 mo, which is unrealistically longer than the 3-6-mo range based on observed atmospheric Hg variability. These results show that the HgI and HgII photoreduction processes largely offset the efficiency of bromine-initiated Hg0 oxidation and reveal missing Hg oxidation processes in the troposphere.

Dissociation of Mercuric Oxides Drives Anomalous Isotope Fractionation during Net Photo-oxidation of Mercury Vapor in Air.

The atmosphere is the primary medium for long-distance transport and transformation of elemental mercury (Hg), a potent neurotoxin. The recent discovery of mass-independent fractionation (MIF) of even-mass Hg isotopes (even-MIF, measured as Δ200Hg and Δ204Hg) in the atmosphere is surprising and can potentially serve as a powerful tracer in understanding Hg biogeochemistry. Far-ultraviolet (UVC) light-induced gas-phase reactions have been suspected as a likely cause for even-MIF, yet the mechanism remains unknown. Here, we present the first experimental evidence of large-scale even-MIF caused by UVC-induced (wavelength: 254 nm) Hg oxidation in synthetic air at the pressure (46-88 kPa) and temperature (233-298 K) resembling those of the lower atmosphere. We observe negatively correlated Δ200Hg and Δ204Hg signatures with values as low as -50‰ and as high as 550‰, respectively, in the remaining atomic Hg pool. The magnitude of even-MIF signatures decreases with decreasing pressure with the Δ200Hg/Δ204Hg ratio being similar to that observed in global precipitation. This even-MIF can be explained by photodissociation of mercuric oxides that are photochemically formed in the UVC-irradiated Hg-O2 system. We propose that similar processes occurring in the atmosphere, where mercuric oxide species serve as intermediates, are responsible for the observed even-MIF in the environment.

A New Parallel Intelligence Based Light Field Dataset for Depth Refinement and Scene Flow Estimation.

Computer vision tasks, such as motion estimation, depth estimation, object detection, etc., are better suited to light field images with more structural information than traditional 2D monocular images. However, since costly data acquisition instruments are difficult to calibrate, it is always hard to obtain real-world scene light field images. The majority of the datasets for static light field images now available are modest in size and cannot be used in methods such as transformer to fully leverage local and global correlations. Additionally, studies on dynamic situations, such as object tracking and motion estimates based on 4D light field images, have been rare, and we anticipate a superior performance. In this paper, we firstly propose a new static light field dataset that contains up to 50 scenes and takes 8 to 10 perspectives for each scene, with the ground truth including disparities, depths, surface normals, segmentations, and object poses. This dataset is larger scaled compared to current mainstream datasets for depth estimation refinement, and we focus on indoor and some outdoor scenarios. Second, to generate additional optical flow ground truth that indicates 3D motion of objects in addition to the ground truth obtained in static scenes in order to calculate more precise pixel level motion estimation, we released a light field scene flow dataset with dense 3D motion ground truth of pixels, and each scene has 150 frames. Thirdly, by utilizing the DistgDisp and DistgASR, which decouple the angular and spatial domain of the light field, we perform disparity estimation and angular super-resolution to evaluate the performance of our light field dataset. The performance and potential of our dataset in disparity estimation and angular super-resolution have been demonstrated by experimental results.

Parallel Radars: From Digital Twins to Digital Intelligence for Smart Radar Systems.

Radar is widely employed in many applications, especially in autonomous driving. At present, radars are only designed as simple data collectors, and they are unable to meet new requirements for real-time and intelligent information processing as environmental complexity increases. It is inevitable that smart radar systems will need to be developed to deal with these challenges and digital twins in cyber-physical systems (CPS) have proven to be effective tools in many aspects. However, human involvement is closely related to radar technology and plays an important role in the operation and management of radars; thus, digital twins' radars in CPS are insufficient to realize smart radar systems due to the inadequate consideration of human factors. ACP-based parallel intelligence in cyber-physical-social systems (CPSS) is used to construct a novel framework for smart radars, called Parallel Radars. A Parallel Radar consists of three main parts: a Descriptive Radar for constructing artificial radar systems in cyberspace, a Predictive Radar for conducting computational experiments with artificial systems, and a Prescriptive Radar for providing prescriptive control to both physical and artificial radars to complete parallel execution. To connect silos of data and protect data privacy, federated radars are proposed. Additionally, taking mines as an example, the application of Parallel Radars in autonomous driving is discussed in detail, and various experiments have been conducted to demonstrate the effectiveness of Parallel Radars.

Mercury transformation processes in nature: Critical knowledge gaps and perspectives for moving forward.

The transformation of mercury (Hg) in the environment plays a vital role in the cycling of Hg and its risk to the ecosystem and human health. Of particular importance are Hg oxidation/reduction and methylation/demethylation processes driven or mediated by the dynamics of light, microorganisms, and organic carbon, among others. Advances in understanding those Hg transformation processes determine our capacity of projecting and mitigating Hg risk. Here, we provide a critical analysis of major knowledge gaps in our understanding of Hg transformation in nature, with perspectives on approaches moving forward. Our analysis focuses on Hg transformation processes in the environment, as well as emerging methodology in exploring these processes. Future avenues for improving the understanding of Hg transformation processes to protect ecosystem and human health are also explored.

Development of a competency model for general practitioners after standardized residency training in China by a modified Delphi method.

BACKGROUND: The "5 + 3" residency training is the main stream general practitioner training program in China. However, a competency model is absent for evaluating the clinical competence attained by general practitioners after training. This study was conducted to develop a consensus set of competencies for general practitioners after standardized residency training in China. METHODS: A modified Delphi process was deployed to develop the competency model, including two stages: (1) generation of an initial set of competencies derived from literature review, behavioral observation of GP-patient consultations, and critical incidents interview of GPs; (2) a 2-round, web-based Delphi survey of experts in general practice, selected using purposive sampling, to prioritize and gain consensus on the essential competencies of GPs. RESULTS: From literature review, behavioral observation, and critical incidents interview, 46 competencies in 7 domains were identified. After two rounds of Delphi survey of 28 participants (the mean age was 47.9 [9.3] years and 64.3% were women) representing a range of health professionals (GPs, managers, and researchers), a consensus was reached on 50 competencies categorized into 7 domains. CONCLUSION: A consensus-based competency model for general practitioners in China has been identified which may be used to evaluate the general practitioners' clinical competence after standardized training.

Pay attention to doctor-patient dialogues: Multi-modal knowledge graph attention image-text embedding for COVID-19 diagnosis.

The sudden increase in coronavirus disease 2019 (COVID-19) cases puts high pressure on healthcare services worldwide. At this stage, fast, accurate, and early clinical assessment of the disease severity is vital. In general, there are two issues to overcome: (1) Current deep learning-based works suffer from multimodal data adequacy issues; (2) In this scenario, multimodal (e.g., text, image) information should be taken into account together to make accurate inferences. To address these challenges, we propose a multi-modal knowledge graph attention embedding for COVID-19 diagnosis. Our method not only learns the relational embedding from nodes in a constituted knowledge graph but also has access to medical knowledge, aiming at improving the performance of the classifier through the mechanism of medical knowledge attention. The experimental results show that our approach significantly improves classification performance compared to other state-of-the-art techniques and possesses robustness for each modality from multi-modal data. Moreover, we construct a new COVID-19 multi-modal dataset based on text mining, consisting of 1393 doctor-patient dialogues and their 3706 images (347 X-ray + 2598 CT + 761 ultrasound) about COVID-19 patients and 607 non-COVID-19 patient dialogues and their 10754 images (9658 X-ray + 494 CT + 761 ultrasound), and the fine-grained labels of all. We hope this work can provide insights to the researchers working in this area to shift the attention from only medical images to the doctor-patient dialogue and its corresponding medical images.

A novel framework of collaborative early warning for COVID-19 based on blockchain and smart contracts.

Early warning is a vital component of emergency response systems for infectious diseases. However, most early warning systems are centralized and isolated, thus there are potential risks of single evidence bias and decision-making errors. In this paper, we tackle this issue via proposing a novel framework of collaborative early warning for COVID-19 based on blockchain and smart contracts, aiming to crowdsource early warning tasks to distributed channels including medical institutions, social organizations, and even individuals. Our framework supports two surveillance modes, namely, medical federation surveillance based on federated learning and social collaboration surveillance based on the learning markets approach, and fuses their monitoring results on emerging cases to alert. By using our framework, medical institutions are expected to obtain better federated surveillance models with privacy protection, and social participants without mutual trusts can also share verified surveillance resources such as data and models, and fuse their surveillance solutions. We implemented our proposed framework based on the Ethereum and IPFS platforms. Experimental results show that our framework has advantages of decentralized decision-making, fairness, auditability, and universality. It also has potential guidance and reference value for the early warning and prevention of unknown infectious diseases.

Learning to learn by yourself: Unsupervised meta-learning with self-knowledge distillation for COVID-19 diagnosis from pneumonia cases.

The goal of diagnosing the coronavirus disease 2019 (COVID-19) from suspected pneumonia cases, that is, recognizing COVID-19 from chest X-ray or computed tomography (CT) images, is to improve diagnostic accuracy, leading to faster intervention. The most important and challenging problem here is to design an effective and robust diagnosis model. To this end, there are three challenges to overcome: (1) The lack of training samples limits the success of existing deep-learning-based methods. (2) Many public COVID-19 data sets contain only a few images without fine-grained labels. (3) Due to the explosive growth of suspected cases, it is urgent and important to diagnose not only COVID-19 cases but also the cases of other types of pneumonia that are similar to the symptoms of COVID-19. To address these issues, we propose a novel framework called Unsupervised Meta-Learning with Self-Knowledge Distillation to address the problem of differentiating COVID-19 from pneumonia cases. During training, our model cannot use any true labels and aims to gain the ability of learning to learn by itself. In particular, we first present a deep diagnosis model based on a relation network to capture and memorize the relation among different images. Second, to enhance the performance of our model, we design a self-knowledge distillation mechanism that distills knowledge within our model itself. Our network is divided into several parts, and the knowledge in the deeper parts is squeezed into the shallow ones. The final results are derived from our model by learning to compare the features of images. Experimental results demonstrate that our approach achieves significantly higher performance than other state-of-the-art methods. Moreover, we construct a new COVID-19 pneumonia data set based on text mining, consisting of 2696 COVID-19 images (347 X-ray + 2349 CT), 10,155 images (9661 X-ray + 494 CT) about other types of pneumonia, and the fine-grained labels of all. Our data set considers not only a bacterial infection or viral infection which causes pneumonia but also a viral infection derived from the influenza virus or coronavirus.

Environmental Mercury Chemistry - In Silico.

Mercury (Hg) is a global environmental contaminant. Major anthropogenic sources of Hg emission include gold mining and the burning of fossil fuels. Once deposited in aquatic environments, Hg can undergo redox reactions, form complexes with ligands, and adsorb onto particles. It can also be methylated by microorganisms. Mercury, especially its methylated form methylmercury, can be taken up by organisms, where it bioaccumulates and biomagnifies in the food chain, leading to detrimental effects on ecosystem and human health. In support of the recently enforced Minamata Convention on Mercury, a legally binding international convention aimed at reducing the anthropogenic emission of-and human exposure to-Hg, its global biogeochemical cycle must be understood. Thus, a detailed understanding of the molecular-level interactions of Hg is crucial. The ongoing rapid development of hardware and methods has brought computational chemistry to a point that it can usefully inform environmental science. This is particularly true for Hg, which is difficult to handle experimentally due to its ultratrace concentrations in the environment and its toxicity. The current account provides a synopsis of the application of computational chemistry to filling several major knowledge gaps in environmental Hg chemistry that have not been adequately addressed experimentally. Environmental Hg chemistry requires defining the factors that determine the relative affinities of different ligands for Hg species, as they are critical for understanding its speciation, transformation and bioaccumulation in the environment. Formation constants and the nature of bonding have been determined computationally for environmentally relevant Hg(II) complexes such as chlorides, hydroxides, sulfides and selenides, in various physical phases. Quantum chemistry has been used to determine the driving forces behind the speciation of Hg with hydrochalcogenide and halide ligands. Of particular importance is the detailed characterization of solvation effects. Indeed, the aqueous phase reverses trends in affinities found computationally in the gas phase. Computation has also been used to investigate complexes of methylmercury with (seleno)amino acids, providing a molecular-level understanding of the toxicological antagonism between Hg and selenium (Se). Furthermore, evidence is emerging that ice surfaces play an important role in Hg transport and transformation in polar and alpine regions. Therefore, the diffusion of Hg and its ions through an idealized ice surface has been characterized. Microorganisms are major players in environmental mercury cycling. Some methylate inorganic Hg species, whereas others demethylate methylmercury. Quantum chemistry has been used to investigate catalytic mechanisms of enzymatic Hg methylation and demethylation. The complex interplay between the myriad chemical reactions and transport properties both in and outside microbial cells determines net biogeochemical cycling. Prospects for scaling up molecular work to obtain a mechanistic understanding of Hg cycling with comprehensive multiscale biogeochemical modeling are also discussed.

Underestimated Sink of Atmospheric Mercury in a Deglaciated Forest Chronosequence.

Mercury (Hg) deposition through litterfall has been regarded as the main input of gaseous elemental mercury (Hg0) into forest ecosystems. We hypothesize that earlier studies largely underestimated this sink because the contribution of Hg0 uptake by moss and the downward transport to wood and throughfall is overlooked. To test the hypothesis, we investigated the Hg fluxes contributed via litterfall and throughfall, Hg pool sizes in moss covers and woody biomass as well as their isotopic signatures in a glacier-to-forest succession ecosystem of the Southeast Tibetan Plateau. Results show that Hg0 depositional uptake and pool sizes stored in moss and woody biomass increase rapidly with the time after glacier retreat. Using the flux data as input to a Hg isotopic mixing model, Hg deposition through litterfall accounts for 27-85% of the total accumulation rate of Hg0 in organic soils of glacial retreat over 20-90 years, revealing the presence of additional sources of Hg0 input. Atmospheric Hg0 accounts for 76 ± 24% in ground moss, 86 ± 15% in tree moss, 62-92% in above ground woody biomass (branch-bark-stem), and 44-83% in roots. The downward decreasing gradient of atmospheric Hg0 fractions from the above ground woody biomass to roots suggests a foliage-to-root Hg transport in vegetation after uptake. Additionally, 34-82% of atmospheric Hg0 in throughfall further amplifies the accumulation of Hg0 from atmospheric sources. We conclude that woody biomass, moss, and throughfall represent important Hg0 sinks in forest ecosystems. These previously unaccounted for sink terms significantly increase the previously estimated atmospheric Hg0 sink via litterfall.

Decoupling Natural and Anthropogenic Mercury and Lead Transport from South Asia to the Himalayas.

Mercury (Hg) and lead (Pb) accumulation since the Industrial Revolution has been generally observed to increase concurrently in lake sedimentary records around the world. Located downwind during the monsoon season from the rapidly developing South Asia, the Himalayas and the Tibetan Plateau are expected to receive direct anthropogenic Hg and Pb loadings, yet the source, pathway, and effects of such transport remain poorly known due to logistic challenges in accessing this region. When studying the sediment record from Lake Gokyo (4750 m above sea level (a.s.l.)) in the Himalayas, we find remarkably different Hg and Pb accumulation trends over the past 260 years. Although Hg accumulation has continued to increase since the Industrial Revolution, Pb accumulation peaked during that time and has been decreasing since then. Stable isotope analysis reveals that the decoupling trends between these two elements are due to different sources and pathways of Hg and Pb in the region. Both δ202Hg and Δ199Hg have been increasing since the Industrial Revolution, suggesting that anthropogenic Hg emissions from South Asia have been continuously increasing and that the Indian monsoon-driven wet deposition of atmospheric Hg is the dominant pathway for Hg accumulation in the sediments. In contrast, analysis of 206Pb/207Pb and 208Pb/207Pb ratios suggests that Pb accumulation in the sediments originates primarily from natural sources and that the decreasing trend of Pb accumulation is most likely due to a weakening input of atmospheric mineral dust by the westerlies. These decoupling trends highlight the ongoing issue of transboundary Hg transport to the Himalayas and the Tibetan Plateau that are source waters for major freshwater systems in Asia and calls for regional and international collaborations on Hg emission controls in South Asia.

Awareness, utilization and influencing factors of social supports for main informal caregivers of schizophrenia patients: a cross-sectional study in primary care settings in Beijing, China.

BACKGROUND: Most schizophrenia patients are supported by main informal caregivers at home in China. This study aims to investigate the further needs of social supports for main informal caregivers of schizophrenia patients and to analyze influencing factors on the awareness and utilization of social supports in Beijing. The results of this study could potentially act as reference for health professionals to implement appropriate and effective support programs. METHODS: A mixed-methods approach was used in this study. Awareness, utilization and influencing factors of social supports for main informal caregivers were investigated using questionnaires in 6 urban districts in Beijing. Meanwhile, individual in-depth interviews with 10 main informal caregivers from the urban districts of Beijing were conducted to identify the caregivers' perspective on social supports and their further needs. RESULTS: Quantitative results showed that although the government provided multi-channel resources for schizophrenia patients and their families, awareness and utilization of the resources such as rehabilitation and relevant subsidies were less than 10.0 and 5.0% respectively. Most caregivers in in-depth interviews expressed that they had negative experiences with respect to obtaining social supports, and they emphasized that more support would be needed in terms of financial support, respect, and rehabilitation institutions. CONCLUSIONS: The awareness and utilization of social supports are low for main informal caregivers of schizophrenia patients. More services and improved public attitudes are needed for schizophrenia patients and their caregivers.

Development of type 2 diabetes mellitus quality indicators in general practice by a modified Delphi method in Beijing, China.

BACKGROUND: The service capacity of primary care has improved in China. General practice also takes growing responsibility in the management of type 2 diabetes mellitus, but there are concerns about the paucity of evidence of the quality of care delivered. And there is an absence of systematic quality indicators of type 2 diabetes mellitus in general practice in China. This study aimed to develop a set of type 2 diabetes mellitus quality indicators to facilitate quality measurement in general practice in China. METHODS: Preliminary quality indicators were generated and refined by literature review and an expert consultation meeting. Two rounds of email-based Delphi survey and a consensus meeting were carried out to identify quality indicators. Delphi questionnaires with 43 indicators were sent to 30 participants in the first round. There were 16 general practitioners and 10 community health service center leaders from primary care, 3 endocrinologists and a primary care researcher in the first round. And 27 out of the 30 participants participated in the second round. The consensus meeting was held among 9 participants to refine the indicators and a last round of rating was carried out in the meeting. The indicators were rated in terms of importance and feasibility. The agreement criteria were defined as median ≥ 7.0 and ≥ 85.0% of ratings in the 7-9 tertile for importance; median ≥ 7.0 and ≥ 65.0, 70.0, 75.0% of ratings in the 7-9 tertile for feasibility respectively in the three rounds of rating. RESULTS: After 2 rounds of Delphi survey and the consensus meeting, total 38 indicators achieved consensus for inclusion in the final set of indicators. The final set of indicators were grouped into 7 domains: access (5 indicators), monitoring (12 indicators), health counseling (7 indicators), records (2 indicators), health status (7 indicators), patient satisfaction (2 indicators) and self-management (3 indicators). CONCLUSIONS: A set of 38 potential quality indicators of type 2 diabetes mellitus in general practice were identified by an iterative Delphi process in Beijing, China. Preliminary approach for measurement and data collection were described. However, the indicators still need to be validated by testing in a further study.

Accumulation of Atmospheric Mercury in Glacier Cryoconite over Western China.

Cryoconite is a granular aggregate, comprised of both mineral and biological material, and known to accumulate atmospheric contaminants. In this study, cryoconite was sampled from seven high-elevation glaciers in Western China to investigate the spatial and altitudinal patterns of atmospheric mercury (Hg) accumulation in the cryoconite. The results show that total Hg (HgT) concentrations in cryoconite were significant with relatively higher Hg accumulation in the southern glaciers (66.0 ± 29.3 ng g-1), monsoon-influenced regions, than those in the northern glaciers (42.5 ± 20.7 ng g-1), westerlies-influenced regions. The altitudinal profile indicates that HgT concentrations in the northern glaciers decrease significantly with altitude, while those in the southern glaciers generally increase toward higher elevations. Unexpectedly high accumulation of methyl-Hg (MeHg) with an average of 1.0 ± 0.4 ng g-1 was also detected in the cryoconite samples, revealing the surface of cryoconite could act as a potential site for Hg methylation in alpine environments. Our preliminary estimate suggests a storage of ∼34.3 ± 17.4 and 0.65 ± 0.28 kg of HgT and MeHg from a single year of formation process in the glacier cryoconite. Therefore, glacier cryoconite could play an important role in Hg storage and transformation, which may result in downstream effects on glacier-fed ecosystems under climate warming scenario.

Atmospheric Mercury Depositional Chronology Reconstructed from Lake Sediments and Ice Core in the Himalayas and Tibetan Plateau.

Alpine lake sediments and glacier ice cores retrieved from high mountain regions can provide long-term records of atmospheric deposition of anthropogenic contaminants such as mercury (Hg). In this study, eight lake sediment cores and one glacier ice core were collected from high elevations across the Himalaya-Tibet region to investigate the chronology of atmospheric Hg deposition. Consistent with modeling results, the sediment core records showed higher Hg accumulation rates in the southern slopes of the Himalayas than those in the northern slopes in the recent decades (post-World War II). Despite much lower Hg accumulation rates obtained from the glacier ice core, the temporal trend in the Hg accumulation rates matched very well with that observed from the sediment cores. The combination of the lake sediments and glacier ice core allowed us to reconstruct the longest, high-resolution atmospheric Hg deposition chronology in High Asia. The chronology showed that the Hg deposition rate was low between the 1500s and early 1800, rising at the onset of the Industrial Revolution, followed by a dramatic increase after World War II. The increasing trend continues to the present-day in most of the records, reflecting the continuous increase in anthropogenic Hg emissions from South Asia.

Total and methylated mercury in Arctic multiyear sea ice.

Mercury is one of the primary contaminants of concern in the Arctic marine ecosystem. While considerable efforts have been directed toward understanding mercury cycling in the Arctic, little is known about mercury dynamics within Arctic multiyear sea ice, which is being rapidly replaced with first-year ice. Here we report the first study on the distribution and potential methylation of mercury in Arctic multiyear sea ice. Based on three multiyear ice cores taken from the eastern Beaufort Sea and McClure Strait, total mercury concentrations ranged from 0.65 to 60.8 pM in bulk ice, with the highest values occurring in the topmost layer (∼40 cm) which is attributed to the dynamics of particulate matter. Methylated mercury concentrations ranged from below the method detection limit (<0.1 pM) to as high as 2.64 pM. The ratio of methylated to total mercury peaked, up to ∼40%, in the mid to bottom sections of the ice, suggesting the potential occurrence of in situ mercury methylation. The annual fluxes of total and methylated mercury into the Arctic Ocean via melt of multiyear ice are estimated to be 420 and 42 kg yr(-1), respectively, representing an important and changing source of mercury and methylmercury into the Arctic Ocean marine ecosystem.