Deciphering potential chemical compounds of gaseous oxidized mercury in Florida, USA

The highest mercury (Hg) wet deposition in the United States of America (USA) occurs along the Gulf of Mexico, and in the southern and central Mississippi River Valley. Gaseous oxidized Hg (GOM) is thought to be a major contributor due to high water solubility and reactivity. Therefore, it is critical to understand concentrations, potential for wet and dry deposition, and GOM compounds present in the air. Concentrations and dry-deposition fluxes of GOM were measured and calculated for Naval Air Station Pensacola Outlying Landing Field (OLF) in Florida using data collected by a Tekran (R) 2537/1130/1135, the University of Nevada Reno Reactive Mercury Active System (UNRRMAS) with cation exchange and nylon membranes, and the Aerohead samplers that use cation-exchange membranes to determine dry deposition. Relationships with Tekran (R) derived data must be interpreted with caution, since the GOM concentrations measured are biased low depending on the chemical compounds in air and interferences with water vapor and ozone. Criteria air pollutants were concurrently measured. This allowed for comparison and better understanding of GOM. In addition to other methods previously applied at OLF, use of the UNRRMAS provided a platform for determination of the chemical compounds of GOM in the air. Results from nylon membranes with thermal desorption analyses indicated seven GOM compounds in this area, including HgBr2, HgCl2, HgO, Hg-nitrogen and sulfur compounds, and two unknown compounds. This indicates that the site is influenced by different gaseous phase reactions and sources. Using back-trajectory analysis during a high-GOM event related to high CO, but average SO2, indicated air parcels moved from the free troposphere and across Arkansas, Mississippi, and Alabama at low elevation (<300 m). This event was initially characterized by HgBr2, followed by a mixture of GOM compounds. Overall, GOM chemistry indicates oxidation reactions with local mobile source pollutants and long-range transport. In order to develop methods to measure GOM concentrations and chemistry, and model dry-deposition processes, the actual GOM compounds need to be known, as well as their corresponding physicochemical properties, such as Henry's Law constants

Deciphering potential chemical compounds of gaseous oxidized mercury in Florida, USA

The highest mercury (Hg) wet deposition in the United States of America (USA) occurs along the Gulf of Mexico, and in the southern and central Mississippi River Valley. Gaseous oxidized Hg (GOM) is thought to be a major contributor due to high water solubility and reactivity. Therefore, it is critical to understand concentrations, potential for wet and dry deposition, and GOM compounds present in the air. Concentrations and dry-deposition fluxes of GOM were measured and calculated for Naval Air Station Pensacola Outlying Landing Field (OLF) in Florida using data collected by a Tekran® 2537/1130/1135, the University of Nevada Reno Reactive Mercury Active System (UNRRMAS) with cation exchange and nylon membranes, and the Aerohead samplers that use cation-exchange membranes to determine dry deposition. Relationships with Tekran®-derived data must be interpreted with caution, since the GOM concentrations measured are biased low depending on the chemical compounds in air and interferences with water vapor and ozone.Criteria air pollutants were concurrently measured. This allowed for comparison and better understanding of GOM.In addition to other methods previously applied at OLF, use of the UNRRMAS provided a platform for determination of the chemical compounds of GOM in the air. Results from nylon membranes with thermal desorption analyses indicated seven GOM compounds in this area, including HgBr2, HgCl2, HgO, Hg–nitrogen and sulfur compounds, and two unknown compounds. This indicates that the site is influenced by different gaseous phase reactions and sources. Using back-trajectory analysis during a high-GOM event related to high CO, but average SO2, indicated air parcels moved from the free troposphere and across Arkansas, Mississippi, and Alabama at low elevation (&amp;lt; 300 m). This event was initially characterized by HgBr2, followed by a mixture of GOM compounds. Overall, GOM chemistry indicates oxidation reactions with local mobile source pollutants and long-range transport.In order to develop methods to measure GOM concentrations and chemistry, and model dry-deposition processes, the actual GOM compounds need to be known, as well as their corresponding physicochemical properties, such as Henry's Law constants.</html

Target-oriented Sentiment Classification with Sequential Cross-modal Semantic Graph

Multi-modal aspect-based sentiment classification (MABSC) is task of classifying the sentiment of a target entity mentioned in a sentence and an image. However, previous methods failed to account for the fine-grained semantic association between the image and the text, which resulted in limited identification of fine-grained image aspects and opinions. To address these limitations, in this paper we propose a new approach called SeqCSG, which enhances the encoder-decoder sentiment classification framework using sequential cross-modal semantic graphs. SeqCSG utilizes image captions and scene graphs to extract both global and local fine-grained image information and considers them as elements of the cross-modal semantic graph along with tokens from tweets. The sequential cross-modal semantic graph is represented as a sequence with a multi-modal adjacency matrix indicating relationships between elements. Experimental results show that the approach outperforms existing methods and achieves state-of-the-art performance on two standard datasets. Further analysis has demonstrated that the model can implicitly learn the correlation between fine-grained information of the image and the text with the given target. Our code is available at https://github.com/zjukg/SeqCSG.Comment: ICANN 2023, https://github.com/zjukg/SeqCS

Development of an Understanding of Reactive Mercury in Ambient Air: A Review

This review focuses on providing the history of measurement efforts to quantify and characterize the compounds of reactive mercury (RM), and the current status of measurement methods and knowledge. RM collectively represents gaseous oxidized mercury (GOM) and that bound to particles. The presence of RM was first recognized through measurement of coal-fired power plant emissions. Once discovered, researchers focused on developing methods for measuring RM in ambient air. First, tubular KCl-coated denuders were used for stack gas measurements, followed by mist chambers and annular denuders for ambient air measurements. For ~15 years, thermal desorption of an annular KCl denuder in the Tekran® speciation system was thought to be the gold standard for ambient GOM measurements. Research over the past ~10 years has shown that the KCl denuder does not collect GOM compounds with equal efficiency, and there are interferences with collection. Using a membrane-based system and an automated system—the Detector for Oxidized mercury System (DOHGS)—concentrations measured with the KCl denuder in the Tekran speciation system underestimate GOM concentrations by 1.3 to 13 times. Using nylon membranes it has been demonstrated that GOM/RM chemistry varies across space and time, and that this depends on the oxidant chemistry of the air. Future work should focus on development of better surfaces for collecting GOM/RM compounds, analytical methods to characterize GOM/RM chemistry, and high-resolution, calibrated measurement systems

DUET: Cross-modal Semantic Grounding for Contrastive Zero-shot Learning

Zero-shot learning (ZSL) aims to predict unseen classes whose samples have never appeared during training. One of the most effective and widely used semantic information for zero-shot image classification are attributes which are annotations for class-level visual characteristics. However, the current methods often fail to discriminate those subtle visual distinctions between images due to not only the shortage of fine-grained annotations, but also the attribute imbalance and co-occurrence. In this paper, we present a transformer-based end-to-end ZSL method named DUET, which integrates latent semantic knowledge from the pre-trained language models (PLMs) via a self-supervised multi-modal learning paradigm. Specifically, we (1) developed a cross-modal semantic grounding network to investigate the model's capability of disentangling semantic attributes from the images; (2) applied an attribute-level contrastive learning strategy to further enhance the model's discrimination on fine-grained visual characteristics against the attribute co-occurrence and imbalance; (3) proposed a multi-task learning policy for considering multi-model objectives. We find that our DUET can achieve state-of-the-art performance on three standard ZSL benchmarks and a knowledge graph equipped ZSL benchmark. Its components are effective and its predictions are interpretable.Comment: AAAI 2023 (Oral). Repository: https://github.com/zjukg/DUE

MEAformer: Multi-modal Entity Alignment Transformer for Meta Modality Hybrid

As an important variant of entity alignment (EA), multi-modal entity alignment (MMEA) aims to discover identical entities across different knowledge graphs (KGs) with relevant images attached. We noticed that current MMEA algorithms all globally adopt the KG-level modality fusion strategies for multi-modal entity representation but ignore the variation in modality preferences for individual entities, hurting the robustness to potential noise involved in modalities (e.g., blurry images and relations). In this paper, we present MEAformer, a multi-modal entity alignment transformer approach for meta modality hybrid, which dynamically predicts the mutual correlation coefficients among modalities for entity-level feature aggregation. A modal-aware hard entity replay strategy is further proposed for addressing vague entity details. Experimental results show that our model not only achieves SOTA performance on multiple training scenarios including supervised, unsupervised, iterative, and low resource, but also has a comparable number of parameters, optimistic speed, and good interpretability. Our code and data are available at https://github.com/zjukg/MEAformer.Comment: Repository: https://github.com/zjukg/MEAforme

Air Quality and Health-Related Impacts of Traditional and Alternate Jet Fuels From Airport Aircraft Operations in the U.S.

13-C-AJFF-UNC-010, 012This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license https://creativecommons.org/licenses/by-nc-nd/4.0/. Please cite this article as: Calvin A. Arter, Jonathan J. Buonocore, Chowdhury Moniruzzaman, Dongmei Yang, Jiaoyan Huang, Saravanan Arunachalam, Air quality and health-related impacts of traditional and alternate jet fuels from airport aircraft operations in the U.S., Environment International, Volume 158, 2022, 106958, ISSN 0160-4120, https://doi.org/10.1016/j.envint.2021.106958.Aviation emissions from landing and takeoff operations (LTO) can degrade local and regional air quality leading to adverse health outcomes in populations near airports and downwind. In this study we aim to quantify the air quality and health-related impacts from commercial LTO emissions in the continental U.S. for two recent years\u2019 inventories, 2011 and 2016. We quantify the LTO-attributable PM2.5, O3, and NO2 concentrations and health outcomes for mortality and multiple morbidity health endpoints. We also quantify the impacts from two scenarios representing a nation-wide implementation of 5% or 50% blends of sustainable alternative jet fuels. We estimate 80 (68\u201393) and 88 (75\u2013100) PM2.5-attributable and 610 (310\u2013920) and 1,100 (570\u20131,700) NO2- attributable premature mortalities in 2011 and 2016, respectively. We estimate a net decrease of 28 (14\u201356) and 54 (27\u2013110) in O3-attributable premature mortalities across the U.S. in 2011 and 2016, respectively due to the large O3 titration effects near the airports. We also find that the asthma exacerbations due to NO2 exposures from LTO emissions increase from 100,000 (2,500\u2013200,000) in 2011 to 170,000 (4,400\u2013340,000) in 2016. Implementing a 5% or 50% blend of sustainable alternative jet fuel in 2016 results in a 1% or 18% reduction, respectively in PM2.5-attributable premature mortalities. Monetizing the value of avoided total premature mortalities, we find that a 50%-blended sustainable alternative jet fuel results in a 19% decrease in PM2.5 damages per ton of fuel burned and a 2% decrease in total damages per ton of fuel burned as compared to damages from traditional jet fuel. We also quantify health impacts by state and find California to be the most impacted by LTO emissions. We find that LTO-attributable PM2.5 and NO2 premature mortalities increase by 10% and 80%, respectively from 2011 to 2016 and that NO2-attributable premature mortalities are responsible for 91% of total LTO-attributable premature mortalities in both 2011 and 2016. And since we find LTO-attributable NO2 to be unaffected by the implementation of sustainable alternative jet fuels, additional approaches focused on NOX reductions in the combustor are needed to mitigate the air quality-related health impacts from LTO emissions

Superior strength of tri-layered Al-Cu-Al nano-composites processed by high-pressure torsion

This investigation demonstrates that a solid-state reaction occurs by the application of high-pressure torsion (HPT) in the production of nanostructured multilayered hybrid Al-Cu systems. Three-layered stacks of Al/Cu/Al were subjected for up to 200 revolutions of HPT under an applied pressure of 6.0 GPa. Microstructural and mechanical properties analysis were carried out after HPT using X-ray diffraction, scanning and transmission electron microscopy, energy dispersive spectrometry (EDX), microhardness measurements and tensile tests. The SEM observations revealed the formation of a multi-nano-layered structure in the whole volume of the disks. Further investigations with the use of TEM demonstrated that each nano-layer consists of nano-grains having sizes of about 20 nm. Analysis by XRD and selected area electron diffraction (SAED) confirmed the formation of intermetallic CuAl2 and Cu9Al4 phases in the layered structures. The experiments also showed a significant improvement in microhardness (up to ~450 Hv) and tensile properties (over 900 MPa of UTS after 200 turns) when compared to both Al-1050 and 99.95%Cu alloys in the initial state and after HPT processing. The results demonstrate that HPT offers an outstanding opportunity for producing novel nanostructured Al-Cu multilayered composites having unique mechanical properties

Evidence for a Free Troposphere Source of Mercury in Wet Deposition in the Western United States

Mercury (Hg) in wet deposition in the United States is measured as part of the National Atmospheric Deposition ProgramHg Deposition Network (MDN). This study focused on compiling and assessing data collected since 2003 at two sites in Nevada, and at one in Nevada and three in Idaho where data were collected over shorter time intervals. At all locations the highest volume weighted concentrations and deposition were observed in the summer, a time of low precipitation, but high gaseous oxidized Hg (GOM) concentrations. Detailed assessment of high Hg wet deposition events showed variability across the region and higher deposition at the higher elevation sites. Potential Source Contribution Function analyses showed no specific point sources associated with the high Hg wet deposition events, but air masses derived predominantly from the southwest. Although this region has potential local and regional sources of GOM, detailed back trajectory analyses, the environmental setting of Nevada, results from other studies in Nevada, and GEOS-Chem modeling results point toward a free troposphere contribution to mercury in wet deposition input to this area of the western United States